{"id":13481469,"url":"https://github.com/ShownX/FacePaperCollection","last_synced_at":"2025-03-27T12:30:53.589Z","repository":{"id":95630149,"uuid":"76701293","full_name":"ShownX/FacePaperCollection","owner":"ShownX","description":"A collection of face related papers","archived":false,"fork":false,"pushed_at":"2019-02-11T21:59:34.000Z","size":17,"stargazers_count":239,"open_issues_count":0,"forks_count":56,"subscribers_count":24,"default_branch":"master","last_synced_at":"2024-10-30T15:50:51.740Z","etag":null,"topics":["face-alignment","face-detection","face-identification","face-recognition","face-reconstruction","face-verification","research-paper"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ShownX.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2016-12-17T03:22:49.000Z","updated_at":"2024-02-20T13:06:36.000Z","dependencies_parsed_at":"2023-05-21T05:15:21.610Z","dependency_job_id":null,"html_url":"https://github.com/ShownX/FacePaperCollection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShownX%2FFacePaperCollection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShownX%2FFacePaperCollection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShownX%2FFacePaperCollection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShownX%2FFacePaperCollection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShownX","download_url":"https://codeload.github.com/ShownX/FacePaperCollection/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245844837,"owners_count":20681786,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["face-alignment","face-detection","face-identification","face-recognition","face-reconstruction","face-verification","research-paper"],"created_at":"2024-07-31T17:00:52.062Z","updated_at":"2025-03-27T12:30:53.316Z","avatar_url":"https://github.com/ShownX.png","language":null,"funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# Face Related Papers and Code Collection\nAny face research/engineer related merge request is wellcome! 02/08/2019.\n\n## Index\n1. [Toolkits](#toolkit)\n2. [Face Detection](#face-detection)\n    - [Survey](#face-detection-survey)\n    - [Datasets](#face-detection-datasets)\n    - [Research](#face-detection-research)\n3. [Face Alignment](#face-alignment)\n    - [Survey](#face-alignment-survey)\n    - [Datasets](#face-alignment-datasets)\n    - [Research](#face-alignment-research)\n4. [Face Recosntruction](#face-reconstruction)\n    - [Survey](#face-reconstruction-survey)\n    - [Datasets](#face-reconstruction-datasets)\n    - [Research](#face-reconstruction-research)\n5. [Face Recognition](#face-recognition)\n    - [Survey](#face-recognition-survey)\n    - [Tutorial](#face-recognition-tutorial)\n    - [Datasets](#face-recognition-datasets)\n    - [Template Generator](#face-recognition-template-generator)\n        - [Pretrained models](#face-recognition-pre-trained-model)\n        - [Image-based Template Generator](#face-recognition-image-template-generator)\n        - [Image-set-based Template Generator](#face-recognition-set-template-generator)\n    - [Face Recognition Pipeline](#face-recognition-pipeline)\n6. [Face Generation](#face-generation)\n    - [Survey](#face-generation-survey)\n    - [Datasets](#face-generation-datasets)\n    - [Research](#face-generation-research)\n7. [Face Attributes Analysis](#face-attributes-analysis)\n    - [Survey](#face-attributes-analysis-survey)\n    - [Datasets](#face-attributes-analysis-datasets)\n    - [Research](#face-attributes-analysis-research)\n\n\n## Toolkits \u003ca name=\"toolkit\"\u003e\u003c/a\u003e\n- FaRE: Open Source Face Recognition Performance Evaluation Package [[Paper](https://arxiv.org/abs/1901.09447)]  [Code is coming soon!]\n- Gluon Toolkit for Face Recognition [[MXNET](https://github.com/THUFutureLab/gluon-face)] \n- Deep Learning:\n    - [MXNet](mxnet.io) and [Gluon](http://gluon.mxnet.io/): A flexible and efficient library for deep learning.\n    - [Torch](torch.ch) and [PyTorch](pytorch.org): Tensors and Dynamic neural networks in Python with strong GPU acceleration.\n    - [TensorFlow](tensorflow.org): An open-source software library for Machine Intelligence.\n    - [Caffe](caffe.berkeleyvision.org) and [Caffe2](https://github.com/caffe2/caffe2): A lightweight, modular, and scalable deep learning framework.\n- Machine Learning:\n    - [Dlib](http://dlib.net/ml.html): A machine learning toolkit.\n- Computer Vision:\n    - [OpenCV](http://opencv.org/): Open Source Computer Vision Library.\n- Probabilistic Programming\n    - [Pyro](https://github.com/uber/pyro): Deep universal probabilistic programming with Python and PyTorch\n\n## Face Detection \u003ca name=\"face-detection\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-detection-survey\"\u003e\u003c/a\u003e\n\n### Datasets \u003ca name=\"face-detection-datasets\"\u003e\u003c/a\u003e\n- [Wildest Faces: Face Detection and Recognition in Violent Settings](https://arxiv.org/abs/1805.07566)\n- [WIDER FACE: A Face Detection Benchmark](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/paper.pdf) [[Project](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html)] \n- [FDDB: Face Detection and Data Set Benchmark](https://www.cics.umass.edu/~elm/papers/fddb.pdf) [[Project](http://vis-www.cs.umass.edu/fddb/)] \n- [AFLW: Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.384.2988\u0026rep=rep1\u0026type=pdf) [[Project](https://lrs.icg.tugraz.at/research/aflw/)] \n\n### Research \u003ca name=\"face-detection-research\"\u003e\u003c/a\u003e\n- PyramidBox: A Context-assisted Single Shot Face Detector [ [Paper](https://arxiv.org/pdf/1803.07737.pdf)]  [[TensorFlow](https://github.com/EricZgw/PyramidBox)]  [[PyTorch](https://github.com/Goingqs/PyramidBox)]  [[MXNet](https://github.com/JJXiangJiaoJun/gluon_PyramidBox)]  \n- Face Attention Network: An Effective Face Detector for the Occluded Faces [[Paper](https://arxiv.org/abs/1711.07246)]  [[PyTorch](https://github.com/rainofmine/Face_Attention_Network)]  \n- FaceNess-Net: Face Detection through Deep Facial Part Responses: [[Paper](https://arxiv.org/pdf/1701.08393.pdf)] \n- S\u003csup\u003e3\u003c/sup\u003eFD: Single Shot Scale-invariant Face Detector [[Paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhang_S3FD_Single_Shot_ICCV_2017_paper.pdf)]  [[Caffe](https://github.com/sfzhang15/SFD)]  [[PyTorch](https://github.com/clcarwin/SFD_pytorch)] \n- Finding Tiny Faces: [[Project](https://www.cs.cmu.edu/~peiyunh/tiny/)]  [[Paper](https://arxiv.org/abs/1612.04402)]  [[MatConvNet + MATLAB](https://github.com/peiyunh/tiny)]  [[TensorFlow](https://github.com/cydonia999/Tiny_Faces_in_Tensorflow)]  [[MXNET](https://github.com/zzw1123/mxnet-finding-tiny-face)] \n- SSH: Single Stage Headless Face Detector: [[Paper](https://arxiv.org/pdf/1708.03979.pdf)]  [[Caffe](https://github.com/mahyarnajibi/SSH)]  [[TensorFlow](https://github.com/DetectionTeamUCAS/SSH_Tensorflow)]  [[MXNET](https://github.com/deepinsight/mxnet-SSH)]  \n- Focal Loss for Dense Object Detection: [[Paper](https://arxiv.org/abs/1708.02002)]  [[Caffe](https://github.com/chuanqi305/FocalLoss)]  [[TensorFlow](https://github.com/ailias/Focal-Loss-implement-on-Tensorflow)]  [[MXNET](https://github.com/unsky/focal-loss)] \n- Face R-CNN: [[Paper](https://arxiv.org/abs/1706.01061)]  [[Caffe](https://github.com/playerkk/face-py-faster-rcnn)] \n- FaceBoxes: A CPU Real-time Face Detector with High Accuracy [[Paper](http://cn.arxiv.org/abs/1708.05234)]  [[Caffe](https://github.com/zeusees/FaceBoxes)]  \n- Multiview Face Detection: [[Paper](https://arxiv.org/abs/1502.02766)]  [[Caffe](https://github.com/guoyilin/FaceDetection_CNN)] \n    \n## Face Alignment \u003ca name=\"face-alignment\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-alignment\"\u003e\u003c/a\u003e\n\n### Datasets \u003ca name=\"face-alignment\"\u003e\u003c/a\u003e\n- LS3D-W: How far are we from solving the 2D \u0026 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) [[Project](https://www.adrianbulat.com/face-alignment)] \n- AFLW: Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. [[Project](https://lrs.icg.tugraz.at/research/aflw/)] \n- 300-W [[Project](https://ibug.doc.ic.ac.uk/resources/300-W/)] \n- 300-VW [[Project](https://ibug.doc.ic.ac.uk/resources/300-VW/)]\n\n### Research \u003ca name=\"face-alignment\"\u003e\u003c/a\u003e\n- FAN: How far are we from solving the 2D \u0026 3D Face Alignment problem? [[Paper](https://arxiv.org/abs/1703.07332)]  [[PyTorch](https://github.com/1adrianb/face-alignment)] \n- JFA: Joint Head Pose Estimation and Face Alignment Framework\nUsing Global and Local CNN Features [[Paper](http://cbl.uh.edu/pub_files/07961802.pdf)] \n- MDM: Mnemonic Descent Method [[Paper](https://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf)]  [[TensorFlow](https://github.com/trigeorgis/mdm)] \n- RDL: Recurrent 3D-2D Dual Learning for Large-pose Facial Landmark Detection [[Paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Xiao_Recurrent_3D-2D_Dual_ICCV_2017_paper.pdf)] \n- PIFA: Pose-invariant 3D face alignment [[Paper](https://arxiv.org/abs/1506.03799)]  [[Code](http://cvlab.cse.msu.edu/project-pifa.html)] \n\n## Face Reconstruction \u003ca name=\"face-reconstruction\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-reconstruction-survey\"\u003e\u003c/a\u003e\n\n### Datasets \u003ca name=\"face-reconstruction-datasets\"\u003e\u003c/a\u003e\n\n### Research \u003ca name=\"face-reconstruction-research\"\u003e\u003c/a\u003e\n- UH-E2FAR: End-to-end 3D face reconstruction with deep neural networks: [[Paper](https://arxiv.org/abs/1704.05020)] \n- Multi-View 3D Face Reconstruction with Deep Recurrent Neural Networks: [[Paper](http://cbl.uh.edu/pub_files/IJCB-2017-PD.pdf)] \n- 3D Face Morphable Models \"In-the-Wild\" [[Paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Booth_3D_Face_Morphable_CVPR_2017_paper.pdf)] \n- 3DMM-CNN [[Paper](https://arxiv.org/pdf/1612.04904.pdf)]  [[Code](https://github.com/anhttran/3dmm_cnn)] \n- VRN [[Paper](https://arxiv.org/pdf/1703.07834.pdf)] [[Code](https://github.com/AaronJackson/vrn)] [[Online Demo](http://cvl-demos.cs.nott.ac.uk/vrn)]\n- 3DFaceNet [[Paper](https://arxiv.org/pdf/1708.00980.pdf)] \n- MoFA: Unsupervised learning for 3D model and pose parameters [[Paper](https://arxiv.org/abs/1703.10580)] \n- 3DMM-STN: Using 3DMM to transfer 2D image to 2D image texture [[Paper](https://arxiv.org/abs/1708.07199)] \n- Dense Semantic and Topological Correspondence of 3D Faces without Landmarks\n- Generating 3D Faces using Convolutional Mesh Autoencoders [[Paper](https://arxiv.org/pdf/1807.10267.pdf)]  [[Code](https://github.com/anuragranj/coma)] \n\n## Face Recognition \u003ca name=\"face-recognition\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-recognition-survey\"\u003e\u003c/a\u003e\n\n### Tutorial \u003ca name=\"face-recognition-tutorial\"\u003e\u003c/a\u003e\n- [Deep Learning for Face Recognition](http://valse.mmcheng.net/deep-learning-for-face-recognition/)\n\n### Datasets \u003ca name=\"face-recognition-datasets\"\u003e\u003c/a\u003e\n#### Training sets:\n- MS-Celeb-1M: Microsoft dataset contains around 1M subjects [[Project](https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/)]  [[Paper](https://arxiv.org/abs/1607.08221)]  \n- CASIA WebFace: 10,575 subjects and 494,414 images [[Project](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html)]  [[Paper](http://arxiv.org/abs/1411.7923)]  \n- CelebA: 202,599 images and 10,177 subjects, 5 landmark locations, 40 binary attributes [[Project](http://mmlab.ie.cuhk.edu.hk/projects/)] \n- VGG-Face2: A large-scale face dataset contains 3.31 million imaes of 9131 identities. [[Project](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/)] \n\n#### Face Verification\n- LFW: Labeled Face in the Wild: 13,000 images and 5749 subjects [[Download](http://vis-www.cs.umass.edu/lfw/)]\n- CFP: Celebrities in Frontal-Profile in the Wild [[Project](http://www.cfpw.io/)]  [[Paper](http://www.cfpw.io/paper.pdf)]\n- MegaFace: 1 Million Faces for Recognition at Scale, 690,572 subjects [[Download](http://megaface.cs.washington.edu/)]\n- Surveillance Face Recognition Challenge [[Project](https://qmul-survface.github.io/)]  [[Paper](https://arxiv.org/abs/1804.09691)] \n\n#### Face Closed-set Identification\n- UHDB31: UHDB31: A Dataset for Better Understanding Face Recognition\nacross Pose and Illumination Variation [[Paper](http://cbl.uh.edu/pub_files/UHDB31_-_CHI_Workshop_-_Final)] \n\n#### Face Open-set Identification\n- IJB-C: IARPA Janus Benchmark-C: Face dataset and protocol [[Paper](https://noblis.org/wp-content/uploads/2018/03/icb2018.pdf)]\n- IJB-B: IARPA Janus Benchmark-B Face Dataset [[Paper](https://www.nist.gov/document/ijbbchallengedocumentationreadmepdf)]\n- IJB-A: Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A [[Paper](https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf)]\n- Unconstrained Face Detection and Open-Set Face Recognition Challenge [[Project](http://vast.uccs.edu/Opensetface/)]  [[Paper](https://arxiv.org/abs/1708.02337)] \n- MegaFace: 1 Million Faces for Recognition at Scale, 690,572 subjects [[Download](http://megaface.cs.washington.edu/)]\n\n### Template Generators \u003ca name=\"face-recognition-template-generator\"\u003e\u003c/a\u003e\n#### Pretrained Models \u003ca name=\"face-recognition-pre-trained-model\"\u003e\u003c/a\u003e\n- ResNet-101, DenseNet-121 provided by [FaRE](https://arxiv.org/abs/1901.09447)\n- ResNet-50,  SE-ResNet-50 provided by [VGG-Face2](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) [[Download](https://github.com/ox-vgg/vgg_face2)]  \n- VGG-16 provided by [VGG-Face](http://www.robots.ox.ac.uk/~vgg/software/vgg_face/)\n- InsightFace [[Download](https://github.com/deepinsight/insightface)]\n\n#### Image-based Template Genearator \u003ca name=\"face-recognition-image-template-generator\"\u003e\u003c/a\u003e\n- Pairwise Relation Network, ECCV18: [[Paper](https://arxiv.org/pdf/1808.04976.pdf)]\n- GridFace: Face Rectification via Learning Local Homography Transformation, ECCV18: [[Paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhou_GridFace_Face_Rectification_ECCV_2018_paper.pdf)] \n- Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition, ECCV18: [[Paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaohang_Zhan_Consensus-Driven_Propagation_in_ECCV_2018_paper.pdf)] \n- Face Recognition with Contrastive Convolution, ECCV18: [[Paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Chunrui_Han_Face_Recognition_with_ECCV_2018_paper.pdf)] \n- FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR15 [[Paper](https://arxiv.org/abs/1503.03832)]  [[TensorFlow](https://github.com/davidsandberg/facenet)] \n- DeepID series, CVPR14: [[DeepID](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf)]  [[DeepID2](http://arxiv.org/abs/1406.4773)]  [[DeepID3](http://arxiv.org/abs/1502.00873)] \n- DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR14: [[Paper](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf)]  \n\n#### Image-set-based Template Generator \u003ca name=\"face-recognition-set-template-generator\"\u003e\u003c/a\u003e\n- [Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaofeng_Liu_Dependency-aware_Attention_Control_ECCV_2018_paper.pdf), ECCV, 2018\n- [Comparator Network](http://openaccess.thecvf.com/content_ECCV_2018/papers/Weidi_Xie_Comparator_Networks_ECCV_2018_paper.pdf), ECCV, 2018 [[Pytorch](https://github.com/yeomko22/ComparatorNetwork_pytorch)] \n\n\n#### Training Loss \u003ca name=\"face-recognition-training-loss\"\u003e\u003c/a\u003e\n- InsightFace (ArcFace): [Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698), ArXiv, 2018 [[MXNet](https://github.com/deepinsight/insightface)] \n- CosFace: [Large Margin Cosine Loss for Deep Face Recognition](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1797.pdf), CVPR, 2018 [[TensorFlow](https://github.com/yule-li/CosFace)]  [[MXNet](https://github.com/deepinsight/insightface)] \n- Ring loss: Convex Feature Normalization for Face Recognition [[Paper](https://arxiv.org/abs/1803.00130)]  [[PyTorch](https://github.com/Paralysis/ringloss)] \n- Git Loss for Deep Face Recognition [[Paper](https://arxiv.org/abs/1807.08512)] \n- A-Softmax Loss (SphereFace) [[Paper](https://arxiv.org/abs/1704.08063)]  [[Caffe](https://github.com/wy1iu/sphereface)] (Caffe) \n- Triplet Loss [[Paper](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf)]  [[Torch](https://github.com/cmusatyalab/openface)]  [[TensorFlow](https://github.com/davidsandberg/facenet)] \n- Center Loss [[Paper](http://ydwen.github.io/papers/WenECCV16.pdf)]  [[Caffe + MATLAB](https://github.com/ydwen/caffe-face)]  [[MXNet](https://github.com/pangyupo/mxnet_center_loss)] \n- Range Loss [[Paper](https://arxiv.org/abs/1611.08976)]  [[Caffe](https://github.com/Charrin/RangeLoss-Caffe)]  \n- L-Softmax [[Paper](https://arxiv.org/abs/1612.02295)]  [[Caffe](https://github.com/wy1iu/LargeMargin_Softmax_Loss)]  [[MXNet](https://github.com/luoyetx/mx-lsoftmax)] \n- Marginal Loss [[Paper](https://ibug.doc.ic.ac.uk/media/uploads/documents/deng_marginal_loss_for_cvpr_2017_paper.pdf)] \n\n### Face Recognition Pipeline \u003ca name=\"face-recognition-pipeline\"\u003e\u003c/a\u003e\n- [UR2D-E:Evaluation of a 3D-aided Pose Invariant 2D Face Recognition System](http://cbl.uh.edu/pub_files/IJCB-2017-XX.pdf)\n- SeetaFaceEngine: An open source C++ face recognition engine. [[C++](https://github.com/seetaface/SeetaFaceEngine)] \n- [OpenFace: Face recognition with Google's FaceNet deep neural network using Torch](http://reports-archive.adm.cs.cmu.edu/anon/anon/2016/CMU-CS-16-118.pdf)]  [[Torch +Python](https://github.com/cmusatyalab/openface)] \n \n## Face Genearation \u003ca name=\"face-generation\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-generation-survey\"\u003e\u003c/a\u003e\n\n### Datasets \u003ca name=\"face-generation-datasets\"\u003e\u003c/a\u003e\n\n### Research \u003ca name=\"face-generation-research\"\u003e\u003c/a\u003e\n1. TP-GAN: [[Paper](https://arxiv.org/abs/1704.04086)]\n2. FF-GAN: [[Paper](https://arxiv.org/abs/1704.06244)]\n3. DR-GAN: [[Paper](http://cvlab.cse.msu.edu/pdfs/Tran_Yin_Liu_CVPR2017.pdf)] [[Website](http://cvlab.cse.msu.edu/project-dr-gan.html)]\n4. BEGAN: Boundary Equilibrium Generative Adversarial Networks [[Paper](https://arxiv.org/abs/1703.10717)]\n\n## Face Attributes Analysis \u003ca name=\"face-attributes-analysis\"\u003e\u003c/a\u003e\n### Survey \u003ca name=\"face-attributes-analysis-survey\"\u003e\u003c/a\u003e\n\n### Datasets \u003ca name=\"face-attributes-analysis-datasets\"\u003e\u003c/a\u003e\n\n### Research \u003ca name=\"face-attributes-analysis-research\"\u003e\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FShownX%2FFacePaperCollection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FShownX%2FFacePaperCollection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FShownX%2FFacePaperCollection/lists"}