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https://github.com/becauseofAI/awesome-face

An awesome face technology repository.
https://github.com/becauseofAI/awesome-face
artificial-intelligence awesome-face deep-learning face face-3d face-action face-anti-spoofing face-benchmark face-clustering face-code face-deblurring face-detection face-expression face-gan face-landmark face-manipulation face-paper face-recognition face-super-resolution machine-learning
Last synced: about 1 month ago
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An awesome face technology repository.
Host: GitHub
URL: https://github.com/becauseofAI/awesome-face
Owner: becauseofAI
License: apache-2.0
Created: 2017-12-13T03:49:27.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2022-06-03T18:06:41.000Z (over 2 years ago)
Last Synced: 2024-05-18T20:51:36.715Z (7 months ago)
Topics: artificial-intelligence, awesome-face, deep-learning, face, face-3d, face-action, face-anti-spoofing, face-benchmark, face-clustering, face-code, face-deblurring, face-detection, face-expression, face-gan, face-landmark, face-manipulation, face-paper, face-recognition, face-super-resolution, machine-learning
Language: HTML
Homepage: https://becauseofAI.github.io/HelloFace
Size: 13.2 MB
Stars: 1,232
Watchers: 79
Forks: 215
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-face-detection-and-recognition - becauseofAI/awesome-face - face?style=social"/> : An awesome face technology repository. (Summary)
ultimate-awesome - awesome-face - An awesome face technology repository. (Other Lists / Monkey C Lists)
README

        # HelloFace [![Mentioned in Awesome HelloFace](https://awesome.re/mentioned-badge.svg)](https://github.com/becauseofAI/HelloFace)   

An Awesome Face Technology Repository. (**Updating**)  

## :trophy: SOTA

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tinaface-strong-but-simple-baseline-for-face/face-detection-on-wider-face-hard)](https://paperswithcode.com/sota/face-detection-on-wider-face-hard?p=tinaface-strong-but-simple-baseline-for-face)  

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/subpixel-heatmap-regression-for-facial/face-alignment-on-wflw)](https://paperswithcode.com/sota/face-alignment-on-wflw?p=subpixel-heatmap-regression-for-facial)  

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/deep-polynomial-neural-networks/face-verification-on-megaface)](https://paperswithcode.com/sota/face-verification-on-megaface?p=deep-polynomial-neural-networks)

## :dart: Highlight



face detection
  



face alignment
  



face recognition
  

## :computer: Website

https://becauseofAI.github.io/HelloFace (Welcome to maintain the website as a contributor through pulling request.)

## :bookmark_tabs: Content

- [Recent Update](#recent-update)

  - [2022-04-05](#2022-04-05)

  - [2020-10-02](#2020-10-02)

  - [2020-01-26](#2020-01-26)

  - [2019-07-11](#2019-07-11)

  - [2019-04-06](#2019-04-06)

  - [2019-01-12](#2019-01-12)

  - [2018-12-01](#2018-12-01)

  - [2018-07-21](#2018-07-21)

  - [2018-04-20](#2018-04-20)

  - [2018-03-28](#2018-03-28)

- [Face Benchmark and Dataset](#face-benchmark-and-dataset)

  - [Face Recognition Data](#face-recognition-data)

  - [Face Detection Data](#face-detection-data)

  - [Face Landmark Data](#face-landmark-data)

  - [Face Attribute Data](#face-attribute-data)

- [Face Recognition](#face-recognition)

- [Face Detection](#face-detection)

- [Face Landmark](#face-landmark)

- [Face Clustering](#face-clustering)

- [Face Expression](#face-expression)

- [Face Action](#face-action)

- [Face 3D](#face-3d)

- [Face GAN](#face-gan)

  - [Face Character](#face-character)

  - [Face Editing](#face-editing)

  - [Face De-Occlusion](#face-de-occlusion)

  - [Face Aging](#face-aging)

  - [Face Drawing](#face-drawing)

  - [Face Generation](#face-generation)

  - [Face Makeup](#face-makeup)

  - [Face Swap](#face-swap)

  - [Face Other](#face-other)

- [Face Deblurring](#face-deblurring)

- [Face Super-Resolution](#face-super-resolution)

- [Face Manipulation](#face-manipulation)

- [Face Anti-Spoofing](#face-anti-spoofing)

- [Face Adversarial Attack](#face-adversarial-attack)

- [Face Cross-Modal](#face-cross-modal)

- [Face Capture](#face-capture)

- [Face Lib and Tool](#face-lib-and-tool)

## 👋 Recent Update

###### 2022-04-05

**CVPR2021**  

- **VirFace**: Enhancing Face Recognition via Unlabeled Shallow Data [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.pdf)  

- **MagFace**: A Universal Representation for Face Recognition and Quality Assessment [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Meng_MagFace_A_Universal_Representation_for_Face_Recognition_and_Quality_Assessment_CVPR_2021_paper.pdf)

- Variational Prototype Learning for Deep Face Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.pdf)

- Cross-Domain Similarity Learning for Face Recognition in Unseen Domains [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.pdf)

- Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.pdf)

- Mitigating Face Recognition Bias via Group Adaptive Classifier [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.pdf)

- Pseudo Facial Generation With Extreme Poses for Face Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Pseudo_Facial_Generation_With_Extreme_Poses_for_Face_Recognition_CVPR_2021_paper.pdf)  

- Dynamic Class Queue for Large Scale Face Recognition in the Wild [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Class_Queue_for_Large_Scale_Face_Recognition_in_the_CVPR_2021_paper.pdf)

- Improving Transferability of Adversarial Patches on Face Recognition With Generative Models [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Improving_Transferability_of_Adversarial_Patches_on_Face_Recognition_With_Generative_CVPR_2021_paper.pdf)

- **WebFace260M**: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_WebFace260M_A_Benchmark_Unveiling_the_Power_of_Million-Scale_Deep_Face_CVPR_2021_paper.pdf)

- **FaceSec**: A Fine-Grained Robustness Evaluation Framework for Face Recognition Systems [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Tong_FaceSec_A_Fine-Grained_Robustness_Evaluation_Framework_for_Face_Recognition_Systems_CVPR_2021_paper.pdf)

- Spherical Confidence Learning for Face Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Spherical_Confidence_Learning_for_Face_Recognition_CVPR_2021_paper.pdf)

- Consistent Instance False Positive Improves Fairness in Face Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Consistent_Instance_False_Positive_Improves_Fairness_in_Face_Recognition_CVPR_2021_paper.pdf)

- **CRFace**: Confidence Ranker for Model-Agnostic Face Detection Refinement [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.pdf)

- **HLA-Face**: Joint High-Low Adaptation for Low Light Face Detection [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_HLA-Face_Joint_High-Low_Adaptation_for_Low_Light_Face_Detection_CVPR_2021_paper.pdf)

- Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Structure-Aware_Face_Clustering_on_a_Large-Scale_Graph_With_107_Nodes_CVPR_2021_paper.pdf)

- **img2pose**: Face Alignment and Detection via 6DoF, Face Pose Estimation [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.pdf)

- **Clusformer**: A Transformer Based Clustering Approach to Unsupervised Large-Scale Face and Visual Landmark Recognition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Clusformer_A_Transformer_Based_Clustering_Approach_to_Unsupervised_Large-Scale_Face_CVPR_2021_paper.pdf)

- Continuous Face Aging via Self-Estimated Residual Age Embedding [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Continuous_Face_Aging_via_Self-Estimated_Residual_Age_Embedding_CVPR_2021_paper.pdf)

- When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.pdf)

- **SDD-FIQA**: Unsupervised Face Image Quality Assessment With Similarity Distribution Distance [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Ou_SDD-FIQA_Unsupervised_Face_Image_Quality_Assessment_With_Similarity_Distribution_Distance_CVPR_2021_paper.pdf)

- **TediGAN**: Text-Guided Diverse Face Image Generation and Manipulation [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.pdf)

- GAN Prior Embedded Network for Blind Face Restoration in the Wild [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_GAN_Prior_Embedded_Network_for_Blind_Face_Restoration_in_the_CVPR_2021_paper.pdf)

- Inverting Generative Adversarial Renderer for Face Reconstruction [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Piao_Inverting_Generative_Adversarial_Renderer_for_Face_Reconstruction_CVPR_2021_paper.pdf)

- Progressive Semantic-Aware Style Transformation for Blind Face Restoration [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Progressive_Semantic-Aware_Style_Transformation_for_Blind_Face_Restoration_CVPR_2021_paper.pdf)

- Towards Real-World Blind Face Restoration With Generative Facial Prior [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.pdf)

- **FaceInpainter**: High Fidelity Face Adaptation to Heterogeneous Domains [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_FaceInpainter_High_Fidelity_Face_Adaptation_to_Heterogeneous_Domains_CVPR_2021_paper.pdf)

- One Shot Face Swapping on Megapixels [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.pdf)

- High-Fidelity and Arbitrary Face Editing [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_High-Fidelity_and_Arbitrary_Face_Editing_CVPR_2021_paper.pdf)

- Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Seeking_the_Shape_of_Sound_An_Adaptive_Framework_for_Learning_CVPR_2021_paper.pdf)

- Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Flow-Guided_One-Shot_Talking_Face_Generation_With_a_High-Resolution_Audio-Visual_Dataset_CVPR_2021_paper.pdf)

- Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Pose-Controllable_Talking_Face_Generation_by_Implicitly_Modularized_Audio-Visual_Representation_CVPR_2021_paper.pdf)

- Monocular Reconstruction of Neural Face Reflectance Fields [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/R_Monocular_Reconstruction_of_Neural_Face_Reflectance_Fields_CVPR_2021_paper.pdf)

- Learning Complete 3D Morphable Face Models From Images and Videos [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/R_Learning_Complete_3D_Morphable_Face_Models_From_Images_and_Videos_CVPR_2021_paper.pdf)

- Riggable 3D Face Reconstruction via In-Network Optimization [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Riggable_3D_Face_Reconstruction_via_In-Network_Optimization_CVPR_2021_paper.pdf)

- Learning To Aggregate and Personalize 3D Face From In-the-Wild Photo Collection [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.pdf)

- Lifting 2D StyleGAN for 3D-Aware Face Generation [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Lifting_2D_StyleGAN_for_3D-Aware_Face_Generation_CVPR_2021_paper.pdf)

- **3DCaricShop**: A Dataset and a Baseline Method for Single-View 3D Caricature Face Reconstruction [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.pdf)

- Pareidolia Face Reenactment [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.pdf)

- Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.pdf)

- Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Spatial-Phase_Shallow_Learning_Rethinking_Face_Forgery_Detection_in_Frequency_Domain_CVPR_2021_paper.pdf)

- Frequency-Aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.pdf)

- Generalizing Face Forgery Detection With High-Frequency Features [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Generalizing_Face_Forgery_Detection_With_High-Frequency_Features_CVPR_2021_paper.pdf)

- Face Forgery Detection by 3D Decomposition [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Face_Forgery_Detection_by_3D_Decomposition_CVPR_2021_paper.pdf)

- Exploring Adversarial Fake Images on Face Manifold [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Exploring_Adversarial_Fake_Images_on_Face_Manifold_CVPR_2021_paper.pdf)

- Representative Forgery Mining for Fake Face Detection [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Representative_Forgery_Mining_for_Fake_Face_Detection_CVPR_2021_paper.pdf)

- Cross Modal Focal Loss for RGBD Face Anti-Spoofing [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.pdf)

- High-Fidelity Face Tracking for AR/VR via Deep Lighting Adaptation [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_High-Fidelity_Face_Tracking_for_ARVR_via_Deep_Lighting_Adaptation_CVPR_2021_paper.pdf)

- Towards High Fidelity Face Relighting With Realistic Shadows [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Towards_High_Fidelity_Face_Relighting_With_Realistic_Shadows_CVPR_2021_paper.pdf)

- **IronMask**: Modular Architecture for Protecting Deep Face Template [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.pdf)

- Face Forensics in the Wild [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Face_Forensics_in_the_Wild_CVPR_2021_paper.pdf)

**ICCV2021**  

- Body-Face Joint Detection via Embedding and Head Hook [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wan_Body-Face_Joint_Detection_via_Embedding_and_Head_Hook_ICCV_2021_paper.pdf)

- Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Adaptive_Label_Noise_Cleaning_With_Meta-Supervision_for_Deep_Face_Recognition_ICCV_2021_paper.pdf)  

- Teacher-Student Adversarial Depth Hallucination To Improve Face Recognition [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Uppal_Teacher-Student_Adversarial_Depth_Hallucination_To_Improve_Face_Recognition_ICCV_2021_paper.pdf)  

- **DAM**: Discrepancy Alignment Metric for Face Recognition [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_DAM_Discrepancy_Alignment_Metric_for_Face_Recognition_ICCV_2021_paper.pdf)

- **SynFace**: Face Recognition With Synthetic Data [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Qiu_SynFace_Face_Recognition_With_Synthetic_Data_ICCV_2021_paper.pdf)

- **PASS**: Protected Attribute Suppression System for Mitigating Bias in Face Recognition [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Dhar_PASS_Protected_Attribute_Suppression_System_for_Mitigating_Bias_in_Face_ICCV_2021_paper.pdf)

- Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Hou_Disentangled_Representation_for_Age-Invariant_Face_Recognition_A_Mutual_Information_Minimization_ICCV_2021_paper.pdf)  

- Learning Facial Representations From the Cycle-Consistency of Face [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Chang_Learning_Facial_Representations_From_the_Cycle-Consistency_of_Face_ICCV_2021_paper.pdf)  

- Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Cao_Personalized_and_Invertible_Face_De-Identification_by_Disentangled_Identity_Information_Manipulation_ICCV_2021_paper.pdf)  

- **ADNet**: Leveraging Error-Bias Towards Normal Direction in Face Alignment [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_ADNet_Leveraging_Error-Bias_Towards_Normal_Direction_in_Face_Alignment_ICCV_2021_paper.pdf)

- Towards Face Encryption by Generating Adversarial Identity Masks [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Towards_Face_Encryption_by_Generating_Adversarial_Identity_Masks_ICCV_2021_paper.pdf)  

- A Latent Transformer for Disentangled Face Editing in Images and Videos [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yao_A_Latent_Transformer_for_Disentangled_Face_Editing_in_Images_and_ICCV_2021_paper.pdf)  

- **Re-Aging GAN**: Toward Personalized Face Age Transformation [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Makhmudkhujaev_Re-Aging_GAN_Toward_Personalized_Face_Age_Transformation_ICCV_2021_paper.pdf)

- Disentangled Lifespan Face Synthesis [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/He_Disentangled_Lifespan_Face_Synthesis_ICCV_2021_paper.pdf)

- Face Image Retrieval With Attribute Manipulation [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Zaeemzadeh_Face_Image_Retrieval_With_Attribute_Manipulation_ICCV_2021_paper.pdf)

- Self-Supervised 3D Face Reconstruction via Conditional Estimation [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wen_Self-Supervised_3D_Face_Reconstruction_via_Conditional_Estimation_ICCV_2021_paper.pdf)  

- Towards High Fidelity Monocular Face Reconstruction With Rich Reflectance Using Self-Supervised Learning and Ray Tracing [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Dib_Towards_High_Fidelity_Monocular_Face_Reconstruction_With_Rich_Reflectance_Using_ICCV_2021_paper.pdf)

- Self-Supervised 3D Face Reconstruction via Conditional Estimation [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wen_Self-Supervised_3D_Face_Reconstruction_via_Conditional_Estimation_ICCV_2021_paper.pdf)  

- Topologically Consistent Multi-View Face Inference Using Volumetric Sampling [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Topologically_Consistent_Multi-View_Face_Inference_Using_Volumetric_Sampling_ICCV_2021_paper.pdf)

- **Fake It Till You Make It**: Face Analysis in the Wild Using Synthetic Data Alone [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Wood_Fake_It_Till_You_Make_It_Face_Analysis_in_the_ICCV_2021_paper.pdf)

- Exploring Temporal Coherence for More General Video Face Forgery Detection [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Exploring_Temporal_Coherence_for_More_General_Video_Face_Forgery_Detection_ICCV_2021_paper.pdf)  

- **OpenForensics**: Large-Scale Challenging Dataset for Multi-Face Forgery Detection and Segmentation In-the-Wild [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Le_OpenForensics_Large-Scale_Challenging_Dataset_for_Multi-Face_Forgery_Detection_and_Segmentation_ICCV_2021_paper.pdf)

- Detection and Continual Learning of Novel Face Presentation Attacks [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Rostami_Detection_and_Continual_Learning_of_Novel_Face_Presentation_Attacks_ICCV_2021_paper.pdf)

- **MeshTalk**: 3D Face Animation From Speech Using Cross-Modality Disentanglement [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.pdf)  

- Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Super-Resolving_Cross-Domain_Face_Miniatures_by_Peeking_at_One-Shot_Exemplar_ICCV_2021_paper.pdf)  

- Multi-Modality Associative Bridging Through Memory: Speech Sound Recollected From Face Video [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Multi-Modality_Associative_Bridging_Through_Memory_Speech_Sound_Recollected_From_Face_ICCV_2021_paper.pdf)  

- **FACIAL**: Synthesizing Dynamic Talking Face With Implicit Attribute Learning [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_FACIAL_Synthesizing_Dynamic_Talking_Face_With_Implicit_Attribute_Learning_ICCV_2021_paper.pdf)  

- **VariTex**: Variational Neural Face Textures [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Buhler_VariTex_Variational_Neural_Face_Textures_ICCV_2021_paper.pdf)  

- Learning High-Fidelity Face Texture Completion Without Complete Face Texture [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Kim_Learning_High-Fidelity_Face_Texture_Completion_Without_Complete_Face_Texture_ICCV_2021_paper.pdf)  

###### 2020-10-02

**ICIP2020**  

- 3D SPARSE DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION

- A Stacking Ensemble for Anomaly Based Client-Specific Face Spoofing Detection

- ADAPTIVE AGGREGATED TRACKLET LINKING FOR MULTI-FACE TRACKING

- ATTENTION SELECTIVE NETWORK FOR FACE SYNTHESIS AND POSE-INVARIANT FACE RECOGNITION

- **EDGE-GAN**: EDGE CONDITIONED MULTI-VIEW FACE IMAGE GENERATION

- EXTRACTING DEEP LOCAL FEATURES TO DETECT MANIPULATED IMAGES OF HUMAN FACES

- FACE AUTHENTICATION FROM GRAYSCALE CODED LIGHT FIELD

- FACE RECOGNITION UNDER LOW ILLUMINATION VIA DEEP FEATURE RECONSTRUCTION NETWORK

- IMPROVING DETECTION AND RECOGNITION OF DEGRADED FACES BY DISCRIMINATIVE FEATURE RESTORATION USING GAN

- **QAMFACE**: QUADRATIC ADDITIVE ANGULAR MARGIN LOSS FOR FACE RECOGNITION

- REALISTIC TALKING FACE SYNTHESIS WITH GEOMETRY-AWARE FEATURE TRANSFORMATION

- TRIPLET DISTILLATION FOR DEEP FACE RECOGNITION  

**ECCV2020**

- **“Look Ma, no landmarks!”** – Unsupervised, Model-based Dense Face Alignment

- Hierarchical Face Aging through Disentangled Latent Characteristics

- Semi-Siamese Training for Shallow Face Learning

- Face Super-Resolution Guided by 3D Facial Priors

- Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting

- **ProgressFace**: Scale-Aware Progressive Learning for Face Detection

- Face Anti-Spoofing with Human Material Perception

- **Beyond 3DMM Space**: Towards Fine-grained 3D Face Reconstruction

- Blind Face Restoration via Deep Multi-scale Component Dictionaries

- Inequality-Constrained and Robust 3D Face Model Fitting

- **BroadFace**: Looking at Tens of Thousands of People at Once for Face Recognition

- Explainable Face Recognition

- **CONFIG**: Controllable Neural Face Image Generation

- **Sub-center ArcFace**: Boosting Face Recognition by Large-scale Noisy Web Faces

- **CelebA-Spoof**: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations

- **Thinking in Frequency**: Face Forgery Detection by Mining Frequency-aware Clues

- Edge-aware Graph Representation Learning and Reasoning for Face Parsing

- Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision

- **CAFE-GAN**: Arbitrary Face Attribute Editing with Complementary Attention Feature

- Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency

- **Generate to Adapt**: Resolution Adaption Network for Surveillance Face Recognition

- **Caption-Supervised Face Recognition**: Training a State-of-the-Art Face Model without Manual Annotation

- Design and Interpretation of Universal Adversarial Patches in Face Detection

- **JNR**: Joint-based Neural Rig Representation for Compact 3D Face Modeling

- On Disentangling Spoof Trace for Generic Face Anti-Spoofing

- Towards causal benchmarking of bias in face analysis algorithms

- Towards Fast, Accurate and Stable 3D Dense Face Alignment

- Face Anti-Spoofing via Disentangled Representation Learning

- Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model

- **MEAD**: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation

- **Margin-Mix**: Semi–Supervised Learning for Face Expression Recognition

- Password-conditioned Anonymization and Deanonymization with Face Identity Transformers

- Improving Face Recognition by Clustering Unlabeled Faces in the Wild

- Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

- **BioMetricNet**: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions

- Eyeglasses 3D shape reconstruction from a single face image

- Deep Cross-species Feature Learning for Animal Face Recognition via Residual Interspecies Equivariant Network

- High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

- **ByeGlassesGAN**: Identity Preserving Eyeglasses Removal for Face Images

- Jointly De-biasing Face Recognition and Demographic Attribute Estimation

- Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

- Improving Face Recognition from Hard Samples via Distribution Distillation Loss

- Manifold Projection for Adversarial Defense on Face Recognition  

**SIGGRAPH2020**

- A System for Efficient 3D Printed Stop-Motion Face Animation

- Accurate Face Rig Approximation With Deep Differential Subspace Reconstruction

- **DeepFaceDrawing**: Deep Generation of Face Images from Sketches

- **The Eyes Have It**: An Integrated Eye and Face Model for Photorealistic Facial Animation  

**IJCAI2020**  

- Biased Feature Learning for Occlusion Invariant Face Recognition

- Reference Guided Face Component Editing

- Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning

- **FakeSpotter**: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces  

**CVPR2020**  

- Cross-Modal Deep Face Normals With Deactivable Skip Connections

- One-Shot Domain Adaptation for Face Generation

- Towards Learning Structure via Consensus for Face Segmentation and Parsing

- **BFBox**: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector

- **Domain Balancing**: Face Recognition on Long-Tailed Domains

- **FReeNet**: Multi-Identity Face Reenactment

- Learning Identity-Invariant Motion Representations for Cross-ID Face Reenactment

- **Global-Local GCN**: Large-Scale Label Noise Cleansing for Face Recognition

- **3FabRec**: Fast Few-Shot Face Alignment by Reconstruction

- Global Texture Enhancement for Fake Face Detection in the Wild

- **CurricularFace**: Adaptive Curriculum Learning Loss for Deep Face Recognition

- On the Detection of Digital Face Manipulation

- Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing

- **ReDA**:Reinforced Differentiable Attribute for 3D Face Reconstruction

- Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning

- **FaceScape**: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

- Interpreting the Latent Space of GANs for Semantic Face Editing

- **Rotate-and-Render**: Unsupervised Photorealistic Face Rotation From Single-View Images

- Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning

- Density-Aware Feature Embedding for Face Clustering

- Learning to Have an Ear for Face Super-Resolution

- Learning Formation of Physically-Based Face Attributes

- **LUVLi Face Alignment**: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood

- Learning Meta Face Recognition in Unseen Domains

- Cross-Spectral Face Hallucination via Disentangling Independent Factors

- Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation

- Data Uncertainty Learning in Face Recognition

- Face X-Ray for More General Face Forgery Detection

- **Vec2Face**: Unveil Human Faces From Their Blackbox Features in Face Recognition

- **FM2u-Net**: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification

- Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction

- Enhanced Blind Face Restoration With Multi-Exemplar Images and Adaptive Spatial Feature Fusion

- **SER-FIQ**: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness

- Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks

- **RDCFace**: Radial Distortion Correction for Face Recognition

- Searching Central Difference Convolutional Networks for Face Anti-Spoofing

- **RetinaFace**: Single-Shot Multi-Level Face Localisation in the Wild

- Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

- **DeeperForensics-1.0**: A Large-Scale Dataset for Real-World Face Forgery Detection

- **GroupFace**: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition

- A Morphable Face Albedo Model

- Learning Oracle Attention for High-Fidelity Face Completion

- Learning Physics-Guided Face Relighting Under Directional Light

- Towards Universal Representation Learning for Deep Face Recognition

- Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition

- **HAMBox**: Delving Into Mining High-Quality Anchors on Face Detection

- Hierarchical Pyramid Diverse Attention Networks for Face Recognition

- Dynamic Face Video Segmentation via Reinforcement Learning

- Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

- Single-Side Domain Generalization for Face Anti-Spoofing  

**AAAI2020** 

- Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation

- Mis-classified Vector Guided Softmax Loss for Face Recognition

- Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

- Learning to Deblur Face Images via Sketch Synthesis

- **FAN-Face**: a simple orthogonal improvement to deep face recognition

- **KPNet**: Towards Minimal Face Detector

- Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

- Regularized Fine-grained Meta Face Anti-spoofing

- **GDFace**: Gated Deformation for Multi-view Face Image Synthesis

- Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose

- **MarioNETte**: Few-shot Face Reenactment Preserving Identity of Unseen Targets

- A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing

- Video Face Super-Resolution with Motion-Adaptive Feedback Cell

- Facial Attribute Capsules for Noise Face Super Resolution

- Joint Super-Resolution and Alignment of Tiny Faces

###### 2020-01-26

- **UGG**: Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

- **PDSN**: Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network

- Attentional Feature-Pair Relation Networks for Accurate Face Recognition

- **PFE**: Probabilistic Face Embeddings

- Towards Interpretable Face Recognition

- **Co-Mining**: Deep Face Recognition With Noisy Labels

- **Fair Loss**: Margin-Aware Reinforcement Learning for Deep Face Recognition

- Discriminatively Learned Convex Models for Set Based Face Recognition

- **DVG**: Dual Variational Generation for Low Shot Heterogeneous Face Recognition

- **CDP**: Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition   

- **BCL**: Video Face Clustering With Unknown Number of Clusters  

- **DeCaFA**: Deep Convolutional Cascade for Face Alignment in the Wild

- **AWing**: Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

- **KDN**: Face Alignment With Kernel Density Deep Neural Network  

- **DF2Net**: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction

- Face Video Deblurring Using 3D Facial Priors

- Semi-Supervised Monocular 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer

- **3DFC**: 3D Face Modeling From Diverse Raw Scan Data  

- Live Face De-Identification in Video

- Face-to-Parameter Translation for Game Character Auto-Creation

- **SC-FEGAN**: Face Editing Generative Adversarial Network With User's Sketch and Color

- **FSGAN**: Subject Agnostic Face Swapping and Reenactment

- **Make a Face**: Towards Arbitrary High Fidelity Face Manipulation

- Face De-Occlusion Using 3D Morphable Model and Generative Adversarial Network

- **FRV**: Face Reconstruction from Voice using Generative Adversarial Networks

- **From Inference to Generation**: End-to-end Fully Self-supervised Generation of Human Face from Speech

- **PFSR**: Progressive Face Super-Resolution via Attention to Facial Landmark

###### 2019-07-11

- Deep face recognition using imperfect facial data

- Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data

- **RegularFace**: Deep Face Recognition via Exclusive Regularization

- **UniformFace**: Learning Deep Equidistributed Representation for Face Recognition

- **P2SGrad**: Refined Gradients for Optimizing Deep Face Models

- **AdaptiveFace**: Adaptive Margin and Sampling for Face Recognition

- **AdaCos**: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations

- Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition

- **NoiseFace**: Noise-Tolerant Paradigm for Training Face Recognition CNNs

- Feature Transfer Learning for Face Recognition With Under-Represented Data

- **Led3D**: A Lightweight and Efficient Deep Approach to Recognizing Low-Quality 3D Faces

- R3 Adversarial Network for Cross Model Face Recognition

- **RetinaFace**: Single-stage Dense Face Localisation in the Wild

- Group Sampling for Scale Invariant Face Detection

- **FA-RPN**: Floating Region Proposals for Face Detection

- **Semantic Alignment**: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection

- Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks

- **LTC**: Learning to Cluster Faces on an Affinity Graph

- **FECNet**: A Compact Embedding for Facial Expression Similarity

- **LBVCNN**: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences

- Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation

- Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection

- **TCAE**: Self-Supervised Representation Learning From Videos for Facial Action Unit Detection

- **JAANet**: Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

- **2DASL**: Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning

- **MVF-Net**: Multi-View 3D Face Morphable Model Regression

- Dense 3D Face Decoding Over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders

- Towards High-Fidelity Nonlinear 3D Face Morphable Model

- Combining 3D Morphable Models: A Large Scale Face-And-Head Model

- Disentangled Representation Learning for 3D Face Shap

- Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking

- **MMFace**: A Multi-Metric Regression Network for Unconstrained Face Reconstruction

- Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

- Boosting Local Shape Matching for Dense 3D Face Correspondence

- **FML**: Face Model Learning From Videos

- **2DASL**: Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning

- **ATVGnet**: Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss

- **Speech2Face**: Learning the Face Behind a Voice

- Unsupervised Face Normalization With Extreme Pose and Expression in the Wild

- **GANFIT**: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction

- **BeautyGAN**: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network

- **FUNIT**: Few-Shot Unsupervised Image-to-Image Translation

- Automatic Face Aging in Videos via Deep Reinforcement Learning

- Attribute-Aware Face Aging With Wavelet-Based Generative Adversarial Networks

- **SAGAN**:Generative Adversarial Network with Spatial Attention for Face Attribute Editing

- **APDrawingGAN**: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs

- **StyleGAN**: A Style-Based Generator Architecture for Generative Adversarial Networks

- 3D Guided Fine-Grained Face Manipulation

- **SemanticComponent**: Semantic Component Decomposition for Face Attribute Manipulation

- **Dataset and Benchmark**: A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

- Deep Tree Learning for Zero-Shot Face Anti-Spoofing

- Decorrelated Adversarial Learning for Age-Invariant Face Recognition

- Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection

- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition 

- **Speech2Face**: Learning the Face Behind a Voice

- **JFDFMR**: Joint Face Detection and Facial Motion Retargeting for Multiple Faces

- **ATVGnet**: Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss

- High-Quality Face Capture Using Anatomical Muscles

- Monocular Total Capture: Posing Face, Body, and Hands in the Wild

- Expressive Body Capture: 3D Hands, Face, and Body From a Single Image

###### 2019-04-06

- **ISRN**: Improved Selective Refinement Network for Face Detection

- **DSFD**: Dual Shot Face Detector

- **PyramidBox++**: High Performance Detector for Finding Tiny Face

- **VIM-FD**: Robust and High Performance Face Detector

- **SHF**: Robust Face Detection via Learning Small Faces on Hard Images

- **SRN**: Selective Refinement Network for High Performance Face Detection

- **SFDet**: Single-Shot Scale-Aware Network for Real-Time Face Detection

- **JFDFMR**: Joint Face Detection and Facial Motion Retargeting for Multiple Faces

- **PFLD**: A Practical Facial Landmark Detector

- **LinkageFace**: Linkage Based Face Clustering via Graph Convolution Network

- **MLT**: Face Recognition: A Novel Multi-Level Taxonomy based Survey

- **GhostVLAD**: GhostVLAD for set-based face recognition

- **DocFace+**: ID Document to Selfie Matching

- **DiF**: Diversity in Faces

- **2018Survey**: Face Recognition: From Traditional to Deep Learning Methods

###### 2019-01-12

- **2018Survey**: Deep Facial Expression Recognition: A Survey

- **2018Survey**: Deep Face Recognition: A Survey

- **SphereFace+(MHE)**: Learning towards Minimum Hyperspherical Energy

- **HyperFace**: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

###### 2018-12-01

- **FRVT**: Face Recognition Vendor Test

- **GANimation**: Anatomically-aware Facial Animation from a Single Image

- **StarGAN**: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

- **Faceswap**: A tool that utilizes deep learning to recognize and swap faces in pictures and videos

- **HF-PIM**: Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization

- **PRNet**: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

- **LAB**: Look at Boundary: A Boundary-Aware Face Alignment Algorithm

- **Super-FAN**: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs

- **Face-Alignment**: How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

- **Face3D**: Python tools for processing 3D face

- **IMDb-Face**: The Devil of Face Recognition is in the Noise

- **AAM-Softmax(CCL)**: Face Recognition via Centralized Coordinate Learning

- **AM-Softmax**: Additive Margin Softmax for Face Verification

- **FeatureIncay**: Feature Incay for Representation Regularization

- **NormFace**: L2 hypersphere embedding for face Verification

- **CocoLoss**: Rethinking Feature Discrimination and Polymerization for Large-scale Recognition

- **L-Softmax**: Large-Margin Softmax Loss for Convolutional Neural Networks

###### 2018-07-21

- **MobileFace**: A face recognition solution on mobile device

- **Trillion Pairs**: Challenge 3: Face Feature Test/Trillion Pairs

- **MobileFaceNets**: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

###### 2018-04-20

- **PyramidBox**: A Context-assisted Single Shot Face Detector

- **PCN**: Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

- **S³FD**: Single Shot Scale-invariant Face Detector

- **SSH**: Single Stage Headless Face Detector

- **NPD**: A Fast and Accurate Unconstrained Face Detector

- **PICO**: Object Detection with Pixel Intensity Comparisons Organized in Decision Trees

- **libfacedetection**: A fast binary library for face detection and face landmark detection in images.

- **SeetaFaceEngine**: SeetaFace Detection, SeetaFace Alignment and SeetaFace Identification.

- **FaceID**: An implementation of iPhone X's FaceID using face embeddings and siamese networks on RGBD images.

###### 2018-03-28

- **InsightFace(ArcFace)**: 2D and 3D Face Analysis Project

- **CosFace**: Large Margin Cosine Loss for Deep Face Recognition

## 🔖 Face Benchmark and Dataset

#### Face Recognition Data

- **DiF**: Diversity in Faces [[project]](https://www.research.ibm.com/artificial-intelligence/trusted-ai/diversity-in-faces/) [[blog]](https://www.ibm.com/blogs/research/2019/01/diversity-in-faces/)

- **FRVT**: Face Recognition Vendor Test [[project]](https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt) [[leaderboard]](https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt-ongoing)

- **IMDb-Face**: The Devil of Face Recognition is in the Noise(**59k people in 1.7M images**) [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Liren_Chen_The_Devil_of_ECCV_2018_paper.pdf "ECCV2018") [[dataset]](https://github.com/fwang91/IMDb-Face)

- **Trillion Pairs**: Challenge 3: Face Feature Test/Trillion Pairs(**MS-Celeb-1M-v1c with 86,876 ids/3,923,399 aligned images  + Asian-Celeb 93,979 ids/2,830,146 aligned images**) [[benckmark]](http://trillionpairs.deepglint.com/overview "DeepGlint") [[dataset]](http://trillionpairs.deepglint.com/data) [[result]](http://trillionpairs.deepglint.com/results)

- **MF2**: Level Playing Field for Million Scale Face Recognition(**672K people in 4.7M images**) [[paper]](https://homes.cs.washington.edu/~kemelmi/ms.pdf "CVPR2017") [[dataset]](http://megaface.cs.washington.edu/dataset/download_training.html) [[result]](http://megaface.cs.washington.edu/results/facescrub_challenge2.html) [[benckmark]](http://megaface.cs.washington.edu/)

- **MegaFace**: The MegaFace Benchmark: 1 Million Faces for Recognition at Scale(**690k people in 1M images**) [[paper]](http://megaface.cs.washington.edu/KemelmacherMegaFaceCVPR16.pdf "CVPR2016") [[dataset]](http://megaface.cs.washington.edu/participate/challenge.html) [[result]](http://megaface.cs.washington.edu/results/facescrub.html) [[benckmark]](http://megaface.cs.washington.edu/)

- **UMDFaces**: An Annotated Face Dataset for Training Deep Networks(**8k people in 367k images with pose, 21 key-points and gender**) [[paper]](https://arxiv.org/pdf/1611.01484.pdf "arXiv2016") [[dataset]](http://www.umdfaces.io/)

- **MS-Celeb-1M**: A Dataset and Benchmark for Large Scale Face Recognition(**100K people in 10M images**) [[paper]](https://arxiv.org/pdf/1607.08221.pdf "ECCV2016") [[dataset]](http://www.msceleb.org/download/sampleset) [[result]](http://www.msceleb.org/leaderboard/iccvworkshop-c1) [[benchmark]](http://www.msceleb.org/) [[project]](https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/)

- **VGGFace2**: A dataset for recognising faces across pose and age(**9k people in 3.3M images**) [[paper]](https://arxiv.org/pdf/1710.08092.pdf "arXiv2017") [[dataset]](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/)

- **VGGFace**: Deep Face Recognition(**2.6k people in 2.6M images**) [[paper]](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf "BMVC2015") [[dataset]](http://www.robots.ox.ac.uk/~vgg/data/vgg_face/)

- **CASIA-WebFace**: Learning Face Representation from Scratch(**10k people in 500k images**) [[paper]](https://arxiv.org/pdf/1411.7923.pdf "arXiv2014") [[dataset]](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html)

- **LFW**: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments(**5.7k people in 13k images**) [[report]](http://vis-www.cs.umass.edu/lfw/lfw.pdf "UMASS2007") [[dataset]](http://vis-www.cs.umass.edu/lfw/#download) [[result]](http://vis-www.cs.umass.edu/lfw/results.html) [[benchmark]](http://vis-www.cs.umass.edu/lfw/)

#### Face Detection Data

- **WiderFace**: WIDER FACE: A Face Detection Benchmark(**400k people in 32k images with a high degree of variability in scale, pose and occlusion**) [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_WIDER_FACE_A_CVPR_2016_paper.pdf "CVPR2016") [[dataset]](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) [[result]](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) [[benchmark]](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/)

- **FDDB**: A Benchmark for Face Detection in Unconstrained Settings(**5k faces in 2.8k images**) [[report]](https://people.cs.umass.edu/~elm/papers/fddb.pdf "UMASS2010") [[dataset]](http://vis-www.cs.umass.edu/fddb/index.html#download) [[result]](http://vis-www.cs.umass.edu/fddb/results.html) [[benchmark]](http://vis-www.cs.umass.edu/fddb/) 

#### Face Landmark Data

- **LS3D-W**: A large-scale 3D face alignment dataset constructed by annotating the images from AFLW, 300VW, 300W and FDDB in a consistent manner with 68 points using the automatic method [[paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Bulat_How_Far_Are_ICCV_2017_paper.pdf "ICCV2017") [[dataset]](https://adrianbulat.com/face-alignment)

- **AFLW**: Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization(**25k faces with 21 landmarks**) [[paper]](https://files.icg.tugraz.at/seafhttp/files/460c7623-c919-4d35-b24e-6abaeacb6f31/koestinger_befit_11.pdf "BeFIT2011") [[benchmark]](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)

#### Face Attribute Data

- **CelebA**: Deep Learning Face Attributes in the Wild(**10k people in 202k images with 5 landmarks and 40 binary attributes per image**) [[paper]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Liu_Deep_Learning_Face_ICCV_2015_paper.pdf "ICCV2015") [[dataset]](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)

## 🔖 Face Recognition

- Live Face De-Identification in Video [[paper]](https://arxiv.org/abs/1911.08348 "ICCV2019")

- **UGG**: Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition [[paper]](https://arxiv.org/abs/1905.02756 "ICCV2019")

- **PDSN**: Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network [[paper]](https://arxiv.org/abs/1908.06290 "ICCV2019") [[code]](https://github.com/linserSnow/PDSN "PyTorch")

- Attentional Feature-Pair Relation Networks for Accurate Face Recognition [[paper]](https://arxiv.org/abs/1908.06255 "ICCV2019")

- Probabilistic Face Embeddings [[paper]](https://arxiv.org/abs/1904.09658 "ICCV2019") [[code]](https://github.com/seasonSH/Probabilistic-Face-Embeddings "TensorFlow") 

- Towards Interpretable Face Recognition [[paper]](https://arxiv.org/abs/1805.00611 "ICCV2019") [[code]](https://github.com/yubangji123/Interpret_FR "TensorFlow") [[project]](http://cvlab.cse.msu.edu/project-interpret-FR)

- **Co-Mining**: Deep Face Recognition With Noisy Labels [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Co-Mining_Deep_Face_Recognition_With_Noisy_Labels_ICCV_2019_paper.pdf "ICCV2019")

- **Fair Loss**: Margin-Aware Reinforcement Learning for Deep Face Recognition [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Fair_Loss_Margin-Aware_Reinforcement_Learning_for_Deep_Face_Recognition_ICCV_2019_paper.pdf "ICCV2019")

- Discriminatively Learned Convex Models for Set Based Face Recognition [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Cevikalp_Discriminatively_Learned_Convex_Models_for_Set_Based_Face_Recognition_ICCV_2019_paper.pdf "ICCV2019")

- **DVG**: Dual Variational Generation for Low Shot Heterogeneous Face Recognition [[paper]](https://arxiv.org/abs/1903.10203 "NeurIPS2019") [[code]](https://github.com/BradyFU/DVG "PyTorch")

- Deep face recognition using imperfect facial data [[paper]](https://www.sciencedirect.com/science/article/pii/S0167739X18331133 "FGCS2019")

- Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhong_Unequal-Training_for_Deep_Face_Recognition_With_Long-Tailed_Noisy_Data_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/zhongyy/Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data "MXNet")

- **RegularFace**: Deep Face Recognition via Exclusive Regularization [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_RegularFace_Deep_Face_Recognition_via_Exclusive_Regularization_CVPR_2019_paper.pdf "CVPR2019")

- **UniformFace**: Learning Deep Equidistributed Representation for Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Duan_UniformFace_Learning_Deep_Equidistributed_Representation_for_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019")

- **P2SGrad**: Refined Gradients for Optimizing Deep Face Models [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_P2SGrad_Refined_Gradients_for_Optimizing_Deep_Face_Models_CVPR_2019_paper.pdf "CVPR2019")

- **AdaptiveFace**: Adaptive Margin and Sampling for Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_AdaptiveFace_Adaptive_Margin_and_Sampling_for_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019")

- **AdaCos**: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_AdaCos_Adaptively_Scaling_Cosine_Logits_for_Effectively_Learning_Deep_Face_CVPR_2019_paper.pdf "CVPR2019") [[code1]](https://github.com/xialuxi/arcface-caffe "Caffe") [[code2]](https://github.com/4uiiurz1/pytorch-adacos "PyTorch")

- Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Low-Rank_Laplacian-Uniform_Mixed_Model_for_Robust_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019")

- **NoiseFace**: Noise-Tolerant Paradigm for Training Face Recognition CNNs [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Hu_Noise-Tolerant_Paradigm_for_Training_Face_Recognition_CNNs_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/huangyangyu/NoiseFace "Caffe")

- Feature Transfer Learning for Face Recognition With Under-Represented Data [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yin_Feature_Transfer_Learning_for_Face_Recognition_With_Under-Represented_Data_CVPR_2019_paper.pdf "CVPR2019")

- **Led3D**: A Lightweight and Efficient Deep Approach to Recognizing Low-Quality 3D Faces [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Mu_Led3D_A_Lightweight_and_Efficient_Deep_Approach_to_Recognizing_Low-Quality_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/muyouhang/Led3D "NULL") [[dataset]](http://irip.buaa.edu.cn/lock3dface/index.html)

- R3 Adversarial Network for Cross Model Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_R3_Adversarial_Network_for_Cross_Model_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019")  

- **MLT**: Face Recognition: A Novel Multi-Level Taxonomy based Survey [[paper]](https://arxiv.org/abs/1901.00713 "arXiv2019")

- **CDP**: Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition [[paper]](https://arxiv.org/abs/1809.01407 "ECCV2018") [[code]](https://github.com/XiaohangZhan/face_recognition_framework "PyTorch")  [[project]](http://mmlab.ie.cuhk.edu.hk/projects/CDP/)

- **GhostVLAD**: GhostVLAD for set-based face recognition [[paper]](https://arxiv.org/abs/1810.09951 "ACCV2018")

- **DocFace+**: ID Document to Selfie Matching [[paper]](https://arxiv.org/abs/1809.05620 "arXiv2018") [[code]](https://github.com/seasonSH/DocFace "TensorFlow")

- **2018Survey**: Face Recognition: From Traditional to Deep Learning Methods [[paper]](https://arxiv.org/abs/1811.00116 "arXiv2018")

- **2018Survey**: Deep Facial Expression Recognition: A Survey [[paper]](https://arxiv.org/abs/1804.08348 "arXiv2018")

- **2018Survey**: Deep Face Recognition: A Survey [[paper]](https://arxiv.org/abs/1804.06655 "arXiv2018")

- **SphereFace+(MHE)**: Learning towards Minimum Hyperspherical Energy [[paper]](https://arxiv.org/abs/1805.09298 "arXiv2018") [[code]](https://github.com/wy1iu/sphereface-plus "Caffe/Matlab")

- **MobileFace**: A face recognition solution on mobile device [[code]](https://github.com/becauseofAI/MobileFace)

- **MobileFaceNets**: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices [[paper]](https://arxiv.org/abs/1804.07573 "arXiv2018") [[code1]](https://github.com/deepinsight/insightface "MXNet") [[code2]](https://github.com/KaleidoZhouYN/mobilefacenet-caffe "Caffe") [[code3]](https://github.com/xsr-ai/MobileFaceNet_TF "TensorFlow") [[code4]](https://github.com/GRAYKEY/mobilefacenet_ncnn "NCNN")

- **FaceID**: An implementation of iPhone X's FaceID using face embeddings and siamese networks on RGBD images. [[code]](https://github.com/normandipalo/faceID_beta "Keras") [[blog]](https://towardsdatascience.com/how-i-implemented-iphone-xs-faceid-using-deep-learning-in-python-d5dbaa128e1d "Medium") 

- **InsightFace(ArcFace)**: 2D and 3D Face Analysis Project [[paper]](https://arxiv.org/abs/1801.07698 "ArcFace: Additive Angular Margin Loss for Deep Face Recognition(arXiv)") [[code1]](https://github.com/deepinsight/insightface "MXNet")[![GitHub stars](https://img.shields.io/github/stars/deepinsight/insightface.svg?logo=github&label=Stars)](https://github.com/deepinsight/insightface) [[code2]](https://github.com/auroua/InsightFace_TF "TensorFlow")

- **AAM-Softmax(CCL)**: Face Recognition via Centralized Coordinate Learning [[paper]](https://arxiv.org/abs/1801.05678 "arXiv2018")

- **AM-Softmax**: Additive Margin Softmax for Face Verification [[paper]](https://arxiv.org/abs/1801.05599 "arXiv2018") [[code1]](https://github.com/happynear/AMSoftmax "Caffe") [[code2]](https://github.com/Joker316701882/Additive-Margin-Softmax "TensorFlow")

- **CosFace**: Large Margin Cosine Loss for Deep Face Recognition [[paper]](https://arxiv.org/abs/1801.09414 "CVPR2018") [[code1]](https://github.com/deepinsight/insightface "MXNet") [[code2]](https://github.com/yule-li/CosFace "TensorFlow")

- **FeatureIncay**: Feature Incay for Representation Regularization [[paper]](https://arxiv.org/abs/1705.10284 "ICLR2018")

- **CocoLoss**: Rethinking Feature Discrimination and Polymerization for Large-scale Recognition [[paper]](http://cn.arxiv.org/abs/1710.00870 "NIPS2017") [[code]](https://github.com/sciencefans/coco_loss "Caffe")

- **NormFace**: L2 hypersphere embedding for face Verification [[paper]](http://www.cs.jhu.edu/~alanlab/Pubs17/wang2017normface.pdf "ACM2017 Multimedia Conference") [[code]](https://github.com/happynear/NormFace "Caffe")

- **SphereFace(A-Softmax)**: Deep Hypersphere Embedding for Face Recognition [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_SphereFace_Deep_Hypersphere_CVPR_2017_paper.pdf "CVPR2017") [[code]](https://github.com/wy1iu/sphereface "Caffe")

- **L-Softmax**: Large-Margin Softmax Loss for Convolutional Neural Networks [[paper]](http://proceedings.mlr.press/v48/liud16.pdf "ICML2016") [[code1]](https://github.com/wy1iu/LargeMargin_Softmax_Loss "Caffe") [[code2]](https://github.com/luoyetx/mx-lsoftmax "MXNet") [[code3]](https://github.com/HiKapok/tf.extra_losses "TensorFlow") [[code4]](https://github.com/auroua/L_Softmax_TensorFlow "TensorFlow") [[code5]](https://github.com/tpys/face-recognition-caffe2 "Caffe2") [[code6]](https://github.com/amirhfarzaneh/lsoftmax-pytorch "PyTorch") [[code7]](https://github.com/jihunchoi/lsoftmax-pytorch "PyTorch")

- **CenterLoss**: A Discriminative Feature Learning Approach for Deep Face Recognition [[paper]](https://ydwen.github.io/papers/WenECCV16.pdf "ECCV2016") [[code1]](https://github.com/ydwen/caffe-face "Caffe") [[code2]](https://github.com/pangyupo/mxnet_center_loss "MXNet") [[code3]](https://github.com/ShownX/mxnet-center-loss "MXNet-Gluon") [[code4]](https://github.com/EncodeTS/TensorFlow_Center_Loss "TensorFlow")

- **OpenFace**: A general-purpose face recognition library with mobile applications [[report]](http://elijah.cs.cmu.edu/DOCS/CMU-CS-16-118.pdf "CMU2016") [[project]](http://cmusatyalab.github.io/openface/) [[code1]](https://github.com/cmusatyalab/openface "Torch") [[code2]](https://github.com/thnkim/OpenFacePytorch "PyTorch")

- **FaceNet**: A Unified Embedding for Face Recognition and Clustering [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf "CVPR2015") [[code]](https://github.com/davidsandberg/facenet "TensorFlow")

- **DeepID3**: DeepID3: Face Recognition with Very Deep Neural Networks [[paper]](https://arxiv.org/abs/1502.00873 "arXiv2015") 

- **DeepID2+**: Deeply learned face representations are sparse, selective, and robust [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sun_Deeply_Learned_Face_2015_CVPR_paper.pdf "CVPR2015")

- **DeepID2**: Deep Learning Face Representation by Joint Identification-Verification [[paper]](https://papers.nips.cc/paper/5416-deep-learning-face-representation-by-joint-identification-verification.pdf "NIPS2014")

- **DeepID**: Deep Learning Face Representation from Predicting 10,000 Classes [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf "CVPR2014")

- **DeepFace**: Closing the gap to human-level performance in face verification [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf "CVPR2014")

- **LBP+Joint Bayes**: Bayesian Face Revisited: A Joint Formulation [[paper]](https://s3.amazonaws.com/academia.edu.documents/31414608/JointBayesian.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1543656042&Signature=k6LefuQnIC2x8gep7yQTxqKgzus%3D&response-content-disposition=inline%3B%20filename%3DBayesian_Face_Revisited_A_Joint_Formulat.pdf "ECCV2012") [[code1]](https://github.com/cyh24/Joint-Bayesian "Python") [[code2]](https://github.com/MaoXu/Joint_Bayesian "Matlab") [[code3]](https://github.com/Glasssix/joint_bayesian "C++/C#")

- **LBPFace**: Face recognition with local binary patterns [[paper]](https://pdfs.semanticscholar.org/3242/0c65f8ef0c5bd83b14c8ae662cbce73e6781.pdf "ECCV2004") [[code]](https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html "OpenCV")

- **FisherFace(LDA)**: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection [[paper]](https://apps.dtic.mil/dtic/tr/fulltext/u2/1015508.pdf "TPAMI1997") [[code]](https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html "OpenCV")

- **EigenFace(PCA)**: Face recognition using eigenfaces [[paper]](http://www.cs.ucsb.edu/~mturk/Papers/mturk-CVPR91.pdf "CVPR1991") [[code]](https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html "OpenCV")

## 🔖 Face Detection

- **RetinaFace**: Single-stage Dense Face Localisation in the Wild [[paper]](https://arxiv.org/abs/1905.00641 "arXiv2019") [[code]](https://github.com/deepinsight/insightface/tree/master/RetinaFace "MXNet")

- Group Sampling for Scale Invariant Face Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ming_Group_Sampling_for_Scale_Invariant_Face_Detection_CVPR_2019_paper.pdf "CVPR2019")

- **FA-RPN**: Floating Region Proposals for Face Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Najibi_FA-RPN_Floating_Region_Proposals_for_Face_Detection_CVPR_2019_paper.pdf "CVPR2019")

- **SFA**: Small Faces Attention Face Detector [[paper]](https://arxiv.org/abs/1812.08402 "SPIC2019") [[code]](https://github.com/shiluo1990/SFA "Caffe")

- **ISRN**: Improved Selective Refinement Network for Face Detection [[paper]](https://arxiv.org/abs/1901.06651 "arXiv2019")

- **DSFD**: Dual Shot Face Detector [[paper]](https://arxiv.org/abs/1810.10220 "CVPR2019") [[code]](https://github.com/TencentYoutuResearch/FaceDetection-DSFD "PyTorch")

- **PyramidBox++**: High Performance Detector for Finding Tiny Face [[paper]](https://arxiv.org/abs/1904.00386 "arXiv2019")

- **VIM-FD**: Robust and High Performance Face Detector [[paper]](https://arxiv.org/abs/1901.02350 "arXiv2019")

- **SHF**: Robust Face Detection via Learning Small Faces on Hard Images [[paper]](https://arxiv.org/abs/1811.11662 "arXiv2018") [[code]](https://github.com/bairdzhang/smallhardface "Caffe")

- **SRN**: Selective Refinement Network for High Performance Face Detection [[paper]](https://arxiv.org/abs/1809.02693 "AAAI2019")

- **SFDet**: Single-Shot Scale-Aware Network for Real-Time Face Detection [[paper]](https://link.springer.com/epdf/10.1007/s11263-019-01159-3?author_access_token=Jjgl-u1CAXPmSKWDljfSBfe4RwlQNchNByi7wbcMAY7Vwo_nrkuFMElF6YSQ0We34tUs42D0dyurcBAD0sJP66n6GBanVgA9qsuvh4Y_Bjf3E_n9_croQ4esS882srfHyUz-L96pU3gu_M30Kk6_XQ%3D%3D "IJCV2019")

- **HyperFace**: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition [[paper]](https://arxiv.org/abs/1603.01249 "TPAMI2019") [[code]](https://github.com/maharshi95/HyperFace "TensorFlow")

- **PyramidBox**: A Context-assisted Single Shot Face Detector [[paper]](https://arxiv.org/pdf/1803.07737.pdf "arXiv2018") [[code]](https://github.com/PaddlePaddle/models/tree/2a6b7dc92f04815f0b298e59030cb779dd0e038c/fluid/face_detction "PaddlePaddle")

- **PCN**: Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks [[paper]](https://arxiv.org/pdf/1804.06039.pdf "CVPR2018") [[code]](https://github.com/Jack-CV/PCN "C++") 

- **S³FD**: Single Shot Scale-invariant Face Detector [[paper]](https://arxiv.org/pdf/1708.05237.pdf "arXiv2017") [[code]](https://github.com/sfzhang15/SFD "Caffe")

- **SSH**: Single Stage Headless Face Detector [[paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Najibi_SSH_Single_Stage_ICCV_2017_paper.pdf "ICCV2017") [[code]](https://github.com/mahyarnajibi/SSH "Caffe")

- **FaceBoxes**: A CPU Real-time Face Detector with High Accuracy [[paper]](https://arxiv.org/pdf/1708.05234.pdf "IJCB2017")[[code1]](https://github.com/zeusees/FaceBoxes "Caffe") [[code2]](https://github.com/lxg2015/faceboxes "PyTorch")

- **TinyFace**: Finding Tiny Faces [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Hu_Finding_Tiny_Faces_CVPR_2017_paper.pdf "CVPR2017") [[project]](https://www.cs.cmu.edu/~peiyunh/tiny/) [[code1]](https://github.com/peiyunh/tiny "MatConvNet") [[code2]](https://github.com/chinakook/hr101_mxnet "MXNet") [[code3]](https://github.com/cydonia999/Tiny_Faces_in_Tensorflow "TensorFlow")

- **MTCNN**: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks [[paper]](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf "SPL2016") [[project]](https://kpzhang93.github.io/MTCNN_face_detection_alignment/) [[code1]](https://github.com/kpzhang93/MTCNN_face_detection_alignment "Caffe") [[code2]](https://github.com/CongWeilin/mtcnn-caffe "Caffe") [[code3]](https://github.com/foreverYoungGitHub/MTCNN "Caffe") [[code4]](https://github.com/Seanlinx/mtcnn "MXNet") [[code5]](https://github.com/pangyupo/mxnet_mtcnn_face_detection "MXNet") [[code6]](https://github.com/TropComplique/mtcnn-pytorch "PyTorch") [[code7]](https://github.com/AITTSMD/MTCNN-Tensorflow "TensorFlow")

- **NPD**: A Fast and Accurate Unconstrained Face Detector [[paper]](http://www.cbsr.ia.ac.cn/users/scliao/papers/Liao-PAMI15-NPD.pdf "TPAMI2015") [[code]](https://github.com/wincle/NPD "C++") [[project]](http://www.cbsr.ia.ac.cn/users/scliao/projects/npdface/index.html)

- **PICO**: Object Detection with Pixel Intensity Comparisons Organized in Decision Trees [[paper]](https://arxiv.org/pdf/1305.4537.pdf "arXiv2014") [[code]](https://github.com/nenadmarkus/pico "C")

- **libfacedetection**: A fast binary library for face detection and face landmark detection in images. [[code]](https://github.com/ShiqiYu/libfacedetection "C++")

- **SeetaFaceEngine**: SeetaFace Detection, SeetaFace Alignment and SeetaFace Identification [[code]](https://github.com/seetaface/SeetaFaceEngine "C++")

## 🔖 Face Landmark

- **DeCaFA**: Deep Convolutional Cascade for Face Alignment in the Wild [[paper]](https://arxiv.org/abs/1904.02549)

- **AWing**: Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression [[paper]](https://arxiv.org/abs/1904.07399 "ICCV2019") [[code]](https://github.com/protossw512/AdaptiveWingLoss "PyTorch")

- **KDN**: Face Alignment With Kernel Density Deep Neural Network [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Face_Alignment_With_Kernel_Density_Deep_Neural_Network_ICCV_2019_paper.pdf "ICCV2019")

- **Semantic Alignment**: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Semantic_Alignment_Finding_Semantically_Consistent_Ground-Truth_for_Facial_Landmark_Detection_CVPR_2019_paper.pdf "CVPR2019")

- Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Robust_Facial_Landmark_Detection_via_Occlusion-Adaptive_Deep_Networks_CVPR_2019_paper.pdf "CVPR2019")

- **PFLD**: A Practical Facial Landmark Detector [[paper]](https://arxiv.org/abs/1902.10859 "arXiv2019") [[project]](https://sites.google.com/view/xjguo/fld) [[code]](https://drive.google.com/file/d/1n1uZPbM9Wz052aVnlc_3L4gjQHiwfj4B/view "APK")

- **PRNet**: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf "ECCV2018") [[code]](https://github.com/YadiraF/PRNet "TensorFlow")

- **LAB**: Look at Boundary: A Boundary-Aware Face Alignment Algorithm [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Look_at_Boundary_CVPR_2018_paper.pdf "CVPR2018") [[project]](https://wywu.github.io/projects/LAB/LAB.html) [[code]](https://github.com/wywu/LAB "Caffe")

- **Face-Alignment**: How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)  [[paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Bulat_How_Far_Are_ICCV_2017_paper.pdf "ICCV2017") [[project]](https://adrianbulat.com/face-alignment) [[code1]](https://github.com/1adrianb/face-alignment "PyTorch") [[code2]](https://github.com/1adrianb/2D-and-3D-face-alignment "Torch7")

- **ERT**: One Millisecond Face Alignment with an Ensemble of Regression Trees [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Kazemi_One_Millisecond_Face_2014_CVPR_paper.pdf "CVPR2014") [[code]](http://dlib.net/imaging.html "Dlib")

## 🔖 Face Clustering

- **BCL**: Video Face Clustering With Unknown Number of Clusters [[paper]](https://arxiv.org/abs/1908.03381 "ICCV2019") [[code]](https://github.com/makarandtapaswi/BallClustering_ICCV2019 "PyTorch")

- **LinkageFace**: Linkage Based Face Clustering via Graph Convolution Network [[paper]](https://arxiv.org/abs/1903.11306 "CVPR2019")

- **LTC**: Learning to Cluster Faces on an Affinity Graph [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Learning_to_Cluster_Faces_on_an_Affinity_Graph_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/yl-1993/learn-to-cluster "PyTorch") 

## 🔖 Face Expression 

- **FECNet**: A Compact Embedding for Facial Expression Similarity [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Vemulapalli_A_Compact_Embedding_for_Facial_Expression_Similarity_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/GerardLiu96/FECNet "Keras")

- **LBVCNN**: Local Binary Volume Convolutional Neural Network for Facial Expression Recognition from Image Sequences [[paper]](https://arxiv.org/abs/1904.07647 "arXiv2019")

## 🔖 Face Action

- Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Joint_Representation_and_Estimator_Learning_for_Facial_Action_Unit_Intensity_CVPR_2019_paper.pdf "CVPR2019")

- Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Niu_Local_Relationship_Learning_With_Person-Specific_Shape_Regularization_for_Facial_Action_CVPR_2019_paper.pdf "CVPR2019")

- **TCAE**: Self-Supervised Representation Learning From Videos for Facial Action Unit Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Self-Supervised_Representation_Learning_From_Videos_for_Facial_Action_Unit_Detection_CVPR_2019_paper.pdf "CVPR2019 Oral") [[code]](https://github.com/mysee1989/TCAE "PyTorch")

- **JAANet**: Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhiwen_Shao_Deep_Adaptive_Attention_ECCV_2018_paper.pdf "ECCV2018") [[code]](https://github.com/ZhiwenShao/JAANet "Caffe")

## 🔖 Face 3D

- **DF2Net**: A Dense-Fine-Finer Network for Detailed 3D Face Reconstruction [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zeng_DF2Net_A_Dense-Fine-Finer_Network_for_Detailed_3D_Face_Reconstruction_ICCV_2019_paper.pdf "ICCV2019") [[code]](https://github.com/xiaoxingzeng/DF2Net "PyTorch")

- Semi-Supervised Monocular 3D Face Reconstruction With End-to-End Shape-Preserved Domain Transfer [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Piao_Semi-Supervised_Monocular_3D_Face_Reconstruction_With_End-to-End_Shape-Preserved_Domain_Transfer_ICCV_2019_paper.pdf "ICCV2019")

- **3DFC**: 3D Face Modeling From Diverse Raw Scan Data [[paper]](https://arxiv.org/abs/1902.04943 "ICCV2019") [[code]](https://github.com/liuf1990/3DFC "PyTorch")

- **2DASL**: Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning [[paper]](https://arxiv.org/abs/1903.09359 "arXiv2019") [[code]](https://github.com/XgTu/2DASL "PyTorch & Matlab")

- **MVF-Net**: Multi-View 3D Face Morphable Model Regression [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_MVF-Net_Multi-View_3D_Face_Morphable_Model_Regression_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/Fanziapril/mvfnet "PyTorch")

- Dense 3D Face Decoding Over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhou_Dense_3D_Face_Decoding_Over_2500FPS_Joint_Texture__Shape_CVPR_2019_paper.pdf "CVPR2019")

- Towards High-Fidelity Nonlinear 3D Face Morphable Model [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tran_Towards_High-Fidelity_Nonlinear_3D_Face_Morphable_Model_CVPR_2019_paper.pdf "CVPR2019") [[project]](http://cvlab.cse.msu.edu/project-nonlinear-3dmm.html)

- Combining 3D Morphable Models: A Large Scale Face-And-Head Model [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ploumpis_Combining_3D_Morphable_Models_A_Large_Scale_Face-And-Head_Model_CVPR_2019_paper.pdf "CVPR2019") 

- Disentangled Representation Learning for 3D Face Shape [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Jiang_Disentangled_Representation_Learning_for_3D_Face_Shape_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/zihangJiang/DR-Learning-for-3D-Face "Keras")

- Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yoon_Self-Supervised_Adaptation_of_High-Fidelity_Face_Models_for_Monocular_Performance_Tracking_CVPR_2019_paper.pdf "CVPR2019")

- **MMFace**: A Multi-Metric Regression Network for Unconstrained Face Reconstruction [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yi_MMFace_A_Multi-Metric_Regression_Network_for_Unconstrained_Face_Reconstruction_CVPR_2019_paper.pdf "CVPR2019")

- **RingNet**: Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Sanyal_Learning_to_Regress_3D_Face_Shape_and_Expression_From_an_CVPR_2019_paper.pdf "CVPR2019")  [[code]](https://github.com/soubhiksanyal/RingNet "TensorFlow") [[project]](https://ringnet.is.tue.mpg.de/)

- Boosting Local Shape Matching for Dense 3D Face Correspondence [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Fan_Boosting_Local_Shape_Matching_for_Dense_3D_Face_Correspondence_CVPR_2019_paper.pdf "CVPR2019")

- **FML**: Face Model Learning From Videos [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Tewari_FML_Face_Model_Learning_From_Videos_CVPR_2019_paper.pdf "CVPR2019")

## 🔖 Face GAN

#### Face Character

- Face-to-Parameter Translation for Game Character Auto-Creation [[paper]](https://arxiv.org/abs/1909.01064 "ICCV2019")

---

#### Face Editing

- **SC-FEGAN**: Face Editing Generative Adversarial Network With User's Sketch and Color [[paper]](https://arxiv.org/abs/1902.06838 "ICCV2019") [[code]](https://github.com/run-youngjoo/SC-FEGAN "TensorFlow")

---

#### Face De-Occlusion

- Face De-Occlusion Using 3D Morphable Model and Generative Adversarial Network [[paper]](https://arxiv.org/abs/1904.06109 "ICCV2019") [[code]](https://github.com/xweiyuan/Face-de-occlusion-using-3D-morphable-model-and-generative-adversarial-network)

---

#### Face Aging  

- Automatic Face Aging in Videos via Deep Reinforcement Learning [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Duong_Automatic_Face_Aging_in_Videos_via_Deep_Reinforcement_Learning_CVPR_2019_paper.pdf "CVPR2019") [[blog]](https://www.fastcompany.com/90314606/this-new-ai-tool-makes-creepily-realistic-videos-of-faces-in-the-future)

- Attribute-Aware Face Aging With Wavelet-Based Generative Adversarial Networks [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Attribute-Aware_Face_Aging_With_Wavelet-Based_Generative_Adversarial_Networks_CVPR_2019_paper.pdf "CVPR2019")

- **SAGAN**:Generative Adversarial Network with Spatial Attention for Face Attribute Editing [[paper]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Gang_Zhang_Generative_Adversarial_Network_ECCV_2018_paper.pdf "ECCV2018") [[code]](https://github.com/elvisyjlin/SpatialAttentionGAN "PyTorch")

---

#### Face Drawing 

- **APDrawingGAN**: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yi_APDrawingGAN_Generating_Artistic_Portrait_Drawings_From_Face_Photos_With_Hierarchical_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/yiranran/APDrawingGAN "PyTorch")

---

#### Face Generation

- **StyleGAN**: A Style-Based Generator Architecture for Generative Adversarial Networks [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/NVlabs/stylegan "TensorFlow") [[dataset]](https://github.com/NVlabs/ffhq-dataset "FFHQ")

---

#### Face Makeup

- **BeautyGAN**: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network [[paper]](http://liusi-group.com/pdf/BeautyGAN-camera-ready_2.pdf "Multimedia Conference, ACM2018") [[code]](https://github.com/Honlan/BeautyGAN "TensorFlow") [[project]](http://liusi-group.com/projects/BeautyGAN) [[poster]](http://liusi-group.com/pdf/BeautyGAN-camera-ready_2_poster.pdf)

---

#### Face Swap

- **FSGAN**: Subject Agnostic Face Swapping and Reenactment [[paper]](https://arxiv.org/abs/1908.05932 "ICCV2019") [[code]](https://github.com/YuvalNirkin) [[project]](https://nirkin.com/fsgan/)

- **Faceswap**: A tool that utilizes deep learning to recognize and swap faces in pictures and videos [[code1]](https://github.com/deepfakes/faceswap "TensorFlow") [[code2]](https://github.com/iperov/DeepFaceLab "TensorFlow/Keras")

- **FUNIT**: Few-Shot Unsupervised Image-to-Image Translation  [[paper]](https://arxiv.org/abs/1905.01723 "arXiv2019") [[code]](https://github.com/NVlabs/FUNIT "PyTorch") [[project]](https://nvlabs.github.io/FUNIT/)

---

#### Face Other

- Unsupervised Face Normalization With Extreme Pose and Expression in the Wild [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Qian_Unsupervised_Face_Normalization_With_Extreme_Pose_and_Expression_in_the_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/mx54039q/fnm "TensorFlow")

- **GANFIT**: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Gecer_GANFIT_Generative_Adversarial_Network_Fitting_for_High_Fidelity_3D_Face_CVPR_2019_paper.pdf "CVPR2019") [[project]](https://github.com/barisgecer/GANFit)

- **HF-PIM**: Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization [[paper]](http://papers.nips.cc/paper/7551-learning-a-high-fidelity-pose-invariant-model-for-high-resolution-face-frontalization.pdf "NIPS2018")

- **Super-FAN**: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Bulat_Super-FAN_Integrated_Facial_CVPR_2018_paper.pdf "CVPR2018 Spotlight")

- **GANimation**: Anatomically-aware Facial Animation from a Single Image [[paper]](https://www.albertpumarola.com/publications/files/pumarola2018ganimation.pdf "ECCV2018 Oral,Best Paper Award Honorable Mention") [[project]](https://www.albertpumarola.com/research/GANimation/index.html) [[code]](https://github.com/albertpumarola/GANimation "PyTorch")

- **StarGAN**: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_StarGAN_Unified_Generative_CVPR_2018_paper.pdf "CVPR2018")

[[code]](https://github.com/yunjey/StarGAN "PyTorch")

- **PGAN**: Progressive Growing of GANs for Improved Quality, Stability, and Variation [[paper]](https://arxiv.org/abs/1710.10196 "ICLR2018")

[[code1]](https://github.com/tkarras/progressive_growing_of_gans "TensorFlow") [[code2]](https://github.com/github-pengge/PyTorch-progressive_growing_of_gans "PyTorch")

## 🔖 Face Deblurring

- Face Video Deblurring Using 3D Facial Priors [[paper]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ren_Face_Video_Deblurring_Using_3D_Facial_Priors_ICCV_2019_paper.pdf "ICCV2019") [[code]](https://github.com/rwenqi/3Dfacedeblurring "TensorFlow")

## 🔖 Face Super-Resolution

- **PFSR**: Progressive Face Super-Resolution via Attention to Facial Landmark [[paper]](https://arxiv.org/abs/1908.08239 "BMVC2019") [[code]](https://github.com/DeokyunKim/Progressive-Face-Super-Resolution "PyTorch")

## 🔖 Face Manipulation

- **Make a Face**: Towards Arbitrary High Fidelity Face Manipulation [[paper]](https://arxiv.org/abs/1908.07191 "ICCV2019")

- 3D Guided Fine-Grained Face Manipulation [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Geng_3D_Guided_Fine-Grained_Face_Manipulation_CVPR_2019_paper.pdf "CVPR2019")

- **SemanticComponent**: Semantic Component Decomposition for Face Attribute Manipulation [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Semantic_Component_Decomposition_for_Face_Attribute_Manipulation_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/yingcong/SemanticComponent) [[demo]](http://appsrv.cse.cuhk.edu.hk/~ycchen/demos/semantic_component.mp4)

## 🔖 Face Anti-Spoofing

- **Dataset and Benchmark**: A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_A_Dataset_and_Benchmark_for_Large-Scale_Multi-Modal_Face_Anti-Spoofing_CVPR_2019_paper.pdf "CVPR2019") [[poster]](http://www.cbsr.ia.ac.cn/users/sfzhang/Shifeng%20Zhang's%20Homepage_files/CVPR2019_CASIA-SURF_Poster.pdf) [[dataset]](https://sites.google.com/qq.com/chalearnfacespoofingattackdete/)

- Deep Tree Learning for Zero-Shot Face Anti-Spoofing [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Deep_Tree_Learning_for_Zero-Shot_Face_Anti-Spoofing_CVPR_2019_paper.pdf "CVPR2019 Oral") 

## 🔖Face Adversarial Attack

- Decorrelated Adversarial Learning for Age-Invariant Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Decorrelated_Adversarial_Learning_for_Age-Invariant_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019") 

- Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.pdf "CVPR2019") 

- Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Efficient_Decision-Based_Black-Box_Adversarial_Attacks_on_Face_Recognition_CVPR_2019_paper.pdf "CVPR2019") 

## 🔖 Face Cross-Modal

- **From Inference to Generation**: End-to-end Fully Self-supervised Generation of Human Face from Speech [[paper]](https://openreview.net/pdf?id=H1guaREYPr "ICLR2020")

- **FRV**: Face Reconstruction from Voice using Generative Adversarial Networks [[paper]](https://papers.nips.cc/paper/8768-face-reconstruction-from-voice-using-generative-adversarial-networks.pdf "NeurIPS2019") [[code]](https://github.com/cmu-mlsp/reconstructing_faces_from_voices "PyTorch") [[poster]](https://ydwen.github.io/papers/WenNeurIPS19-poster.pdf)

- **Speech2Face**: Learning the Face Behind a Voice [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Oh_Speech2Face_Learning_the_Face_Behind_a_Voice_CVPR_2019_paper.pdf "CVPR2019") [[project]](https://speech2face.github.io/)

- **JFDFMR**: Joint Face Detection and Facial Motion Retargeting for Multiple Faces [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chaudhuri_Joint_Face_Detection_and_Facial_Motion_Retargeting_for_Multiple_Faces_CVPR_2019_paper.pdf "CVPR2019")

- **ATVGnet**: Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Hierarchical_Cross-Modal_Talking_Face_Generation_With_Dynamic_Pixel-Wise_Loss_CVPR_2019_paper.pdf "CVPR2019")  [[code]](https://github.com/lelechen63/ATVGnet "PyTorch")

## 🔖 Face Capture

- High-Quality Face Capture Using Anatomical Muscles [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Bao_High-Quality_Face_Capture_Using_Anatomical_Muscles_CVPR_2019_paper.pdf "CVPR2019")

- Monocular Total Capture: Posing Face, Body, and Hands in the Wild [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xiang_Monocular_Total_Capture_Posing_Face_Body_and_Hands_in_the_CVPR_2019_paper.pdf "CVPR2019")  [[code]](https://github.com/CMU-Perceptual-Computing-Lab/MonocularTotalCapture) [[project]](http://domedb.perception.cs.cmu.edu/mtc.html)

- Expressive Body Capture: 3D Hands, Face, and Body From a Single Image [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Pavlakos_Expressive_Body_Capture_3D_Hands_Face_and_Body_From_a_CVPR_2019_paper.pdf "CVPR2019") [[code]](https://github.com/vchoutas/smplify-x "PyTorch") [[project]](https://smpl-x.is.tue.mpg.de/)

## :hammer: Face Lib and Tool

- **Dlib** [[url]](http://dlib.net/imaging.html "Image Processing") [[github]](https://github.com/davisking/dlib "master")

- **OpenCV** [[docs]](https://docs.opencv.org "All Versions") [[github]](https://github.com/opencv/opencv/ "master")

- **Face3D** [[github]](https://github.com/YadiraF/face3d "master")

---

# 
:boom:**Big Bang**:boom:


#### 
**Receptive Field Is Natural Anchor**

#### **Receptive Field Is All You Need**

2K real-time detection is so easy!


  

#### 
[[Paper]](https://arxiv.org/abs/1904.10633) [[MXNet]](https://github.com/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices) [[PyTorch]](https://github.com/becauseofAI/lffd-pytorch) [![GitHub stars](https://img.shields.io/github/stars/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices.svg?logo=github&label=Stars)](https://github.com/YonghaoHe/A-Light-and-Fast-Face-Detector-for-Edge-Devices)