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https://github.com/polarisZhao/awesome-face
π face releated algorithm, dataset and paper
https://github.com/polarisZhao/awesome-face
List: awesome-face
dataset face face-detection face-recognition face-releated-algorithm paper papers
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π face releated algorithm, dataset and paper
- Host: GitHub
- URL: https://github.com/polarisZhao/awesome-face
- Owner: polarisZhao
- License: mit
- Created: 2018-05-13T06:23:30.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-27T15:13:39.000Z (over 5 years ago)
- Last Synced: 2024-05-23T06:27:03.175Z (8 months ago)
- Topics: dataset, face, face-detection, face-recognition, face-releated-algorithm, paper, papers
- Homepage:
- Size: 2.38 MB
- Stars: 882
- Watchers: 46
- Forks: 182
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# awesome-face [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/polarisZhao/awesome-face)
π₯ face releated algorithm, datasets and papers* [ Paper / Algorithm](#-paper--algorithm)
- [Survey](#Survey)
- [2D- Face Recognition](#2d--face-recognition)
- [Face Detection](#face-detection)
- [Face Alignment](#face-alignment)
- [3D face reconstruction](#3D-face-reconstruction)
- [Face attack & Defends](#face-attack--defends)
* [Open source lib](#-open-source-lib)
- [face recognition](#face-recognition)
- [face detection](#face-detection-1)* [Datasets](#-datasets)
- [2D Face Recognition](#2d-face-recognition)
- [video face recognition](#video-face-recognition)
- [3D face recognition](#3d-face-recognition)
- [Anti-spoofing](#anti-spoofing)
- [cross age and cross pose](#cross-age-and-cross-pose)
- [Face Detection](#face-detection-2)
- [Face Attributes](#face-attributes)
- [Others](#others)
* [ Research home(conf & workshop & trans)](#-research-homeconf--workshop--trans)* [ References:](#-references)
## π Paper / Algorithm
#### Survey
- Deep Face Recognition: A Survey [paper](https://arxiv.org/abs/1804.06655)
- Face Recognition: From Traditional to Deep Learning Methods [paper](https://arxiv.org/abs/1811.00116)
- Deep Facial Expression Recognition: A Survey [paper](https://arxiv.org/abs/1804.08348)
- A Survey on Face Detection and Classification for Partially Occluded images [paper](http://ijariie.com/AdminUploadPdf/A_Survey_on_Face_Detection_and_Classification_for_Partially_Occluded_images_ijariie9406.pdf)
- 3D face recognition: a survey [paper](https://link.springer.com/article/10.1186/s13673-018-0157-2)
- Face detection techniques: a review [paper](https://link.springer.com/article/10.1007/s10462-018-9650-2)#### 2D- Face Recognition
![2d_face_reg](./img/face_reg.jpg)
**[1] DeepID1** [**[paper]**](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf)
Deep Learning Face Representation from Predicting 10,000 Classes
**[2] DeepID2** [**[paper]**](https://arxiv.org/abs/1406.4773)
Deep Learning Face Representation by Joint Identification-Verification
**[3] DeepID2+** [**[paper]**](https://arxiv.org/abs/1412.1265)
Deeply learned face representations are sparse, selective, and robust
**[4] DeepIDv3** [**[paper]**](https://arxiv.org/abs/1502.00873)
DeepID3: Face Recognition with Very Deep Neural Networks
**[5] Deep Face** [**[paper]**](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf)
Deep Face Recognition
**[6] Center Loss** [**[paper]**](http://ydwen.github.io/papers/WenECCV16.pdf) [**[code]**](https://github.com/ydwen/caffe-face)
A Discriminative Feature Learning Approach for Deep Face Recognition
**[7]Marginal loss** [**[paper]**](https://www.computer.org/csdl/proceedings-article/cvprw/2017/0733c006/12OmNzayNCT)
Marginal loss for deep face recognition
**[8] Range Loss**[**[paper]**](https://arxiv.org/abs/1611.08976)
Range Loss for Deep Face Recognition with Long-tail
**[9]Contrastive Loss** [**[paper]**]()
Deep learning face representation by joint identification-verification
**[10] FaceNet** [**[paper]**](https://arxiv.org/abs/1503.03832) [[**third-party implemention**]](https://github.com/davidsandberg/facenet)
FaceNet: A Unified Embedding for Face Recognition and Clustering
**[11] NormFace** [**[paper]**](https://arxiv.org/pdf/1704.06369.pdf) [**[code]**](https://github.com/happynear/NormFace)
NormFace: L2 Hypersphere Embedding for Face Verification
**[12] COCO Loss:** [**[paper]**](https://arxiv.org/pdf/1710.00870.pdf) [[**code**]](https://github.com/sciencefans/coco_loss)
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
**[13] Large-Margin Softmax Loss** [**[paper]**](https://arxiv.org/pdf/1612.02295.pdf) [[**code**]](https://github.com/wy1iu/LargeMargin_Softmax_Loss)
Large-Margin Softmax Loss for Convolutional Neural Networks(L-Softmax loss)
**[14]SphereFaceοΌ** **A-Softmax** [**[paper]**](https://arxiv.org/abs/1704.08063) [[**code**]](https://github.com/wy1iu/sphereface)
SphereFace: Deep Hypersphere Embedding for Face Recognition
**[15]AM-Softmax/cosFace** [**[paper AM-Softmax]**](https://arxiv.org/pdf/1801.05599.pdf) [**[paper cosFace]**](https://arxiv.org/pdf/1801.09414.pdf) [[**AM-softmax code**]](https://github.com/happynear/AMSoftmax)
AM : Additive Margin Softmax for Face Verification
CosFace: Large Margin Cosine Loss for Deep Face Recognition(Tencent AI Lab)
**[16] ArcFace:** [**[paper]**](https://arxiv.org/pdf/1801.07698.pdf) [**[code]**](https://github.com/deepinsight/insightface )
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
**[17] Adaptive Face** [paper](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/papers/2019adaptiveface.pdf)
Adaptive Margin and Sampling for Face Recognition
**[18] AdaCos** [Paper](https://arxiv.org/abs/1905.00292)
Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
**[20] RegularFace**: [paper](http://mftp.mmcheng.net/Papers/19cvprRegularFace.pdf)
Deep Face Recognition via Exclusive Regularization
**[21] UniformFace**: [paper](http://ivg.au.tsinghua.edu.cn/people/Yueqi_Duan/CVPR19_UniformFace%20Learning%20Deep%20Equidistributed%20Representation%20for%20Face%20Recognition.pdf)
Learning Deep Equidistributed Representation for Face Recognition
**[22] P2SGrad**: [paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_P2SGrad_Refined_Gradients_for_Optimizing_Deep_Face_Models_CVPR_2019_paper.pdf)
Refined Gradients for Optimizing Deep Face Models
![cos_loss](./img/cos_loss.jpg)
#### Face Detection
![](./img/face_detection.jpg)
**[1] Cascade CNN** [**[paper]**](https://ieeexplore.ieee.org/document/7299170/) [**[code]**](https://github.com/anson0910/CNN_face_detection)
A Convolutional Neural Network Cascade for Face Detection
**[2] MTCNN** [**[Paper]**](https://kpzhang93.github.io/MTCNN_face_detection_alignment/) [**[code]**](https://github.com/kpzhang93/MTCNN_face_detection_alignment)
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
**[3] ICC - CNN** [**[paper]**](https://ieeexplore.ieee.org/document/8237606)
Detecting Faces Using Inside Cascaded Contextual CNN
**[4] Face R-CNN** [**[Paper]**](https://arxiv.org/pdf/1706.01061.pdf)
Face R-CNN
**[5] Deep-IR**[**[Paper]**](https://arxiv.org/abs/1701.08289)
Face Detection using Deep Learning: An Improved Faster RCNN Approach
**[6] SSH** [**[paper]**](https://arxiv.org/pdf/1708.03979.pdf) [**[code]**](https://github.com/mahyarnajibi/SSH)
SSH: Single Stage Headless Face Detector
**[7] S3FD** [**[paper]**](https://arxiv.org/abs/1708.05237)
Single Shot Scale-invariant Face Detector
**[8] FaceBoxes** [**[paper]**](https://arxiv.org/pdf/1708.05234.pdf) [**[code]**](https://github.com/sfzhang15/FaceBoxes)
Faceboxes: A CPU Real-time Face Detector with High Accuracy
**[9] Scaleface** [**[paper]**](http://cn.arxiv.org/abs/1706.02863)
Face Detection through Scale-Friendly Deep Convolutional Networks
**[10] HR** [**[paper]**](https://arxiv.org/abs/1612.04402) [**[code]**](https://github.com/peiyunh/tiny)
Finding Tiny Faces
**[11] FAN** [**[paper]**](https://arxiv.org/abs/1712.00721)
Feature Agglomeration Networks for Single Stage Face Detection.
**[12] PyramidBox** [**[paper]**](https://arxiv.org/abs/1803.07737?context=cs) [**[code]**](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleCV/face_detection/README_cn.md)
PyramidBox: A Context-assisted Single Shot Face Detector
**[13] SRN** [**[paper]**](https://arxiv.org/abs/1809.02693)
Selective Refinement Network for High Performance Face Detection.
**[14] DSFD** [**[paper]**](https://arxiv.org/abs/1810.10220)
DSFD: Dual Shot Face Detector
**[15] VIM FD** [**[paper]**](https://arxiv.org/abs/1901.02350)
Robust and High Performance Face Detector
**[16] ISRN** [**[paper]**](https://arxiv.org/abs/1901.06651)
Improved Selective Refinement Network for Face Detection
**[17] PyramidBox++** [**[Paper]**](https://arxiv.org/abs/1904.00386)
PyramidBox++: High Performance Detector for Finding Tiny Face
**[18] RetinaFace** [**[paper]**](https://arxiv.org/pdf/1905.00641.pdf) [**[code]**](https://github.com/deepinsight/insightface/tree/master/RetinaFace)
RetinaFace: Single-stage Dense Face Localisation in the Wild
#### Face Alignment
**[1] PRNet** [**[paper]**](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf) [**[code]**](https://github.com/YadiraF/PRNet)
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
**[2]LAB** [Paper](https://arxiv.org/abs/1805.10483) [**[code]**](https://github.com/wywu/LAB)
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
**[3]PFLD** [Paper](https://arxiv.org/pdf/1902.10859.pdf) [**[demo code]**](https://sites.google.com/view/xjguo/fld)
PFLD: A Practical Facial Landmark Detector
**[4] 2D & 3D FAN** [**[Paper]**](https://www.adrianbulat.com/downloads/FaceAlignment/FaceAlignment.pdf) [**[code]**](https://github.com/1adrianb/face-alignment)
How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
#### 3D face reconstruction
**[1] 3DMM**
A Morphable Model For The Synthesis Of 3D Faces
**[2] 3DDFA** [**[paper]**](https://arxiv.org/abs/1804.01005) [**[github]**](https://github.com/cleardusk/3DDFA)
Face Alignment in Full Pose Range: A 3D Total Solution.
**[3] VRN** [**[index]**](http://aaronsplace.co.uk/papers/jackson2017recon/index.html) [**[code]**](https://github.com/AaronJackson/vrn)
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression(3D Face Reconstruction from a Single Image)
**[4] PRNet** [**[paper]**](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yao_Feng_Joint_3D_Face_ECCV_2018_paper.pdf) [**[github]**](https://github.com/YadiraF/PRNet)
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
**[5] 2DASL** [**[paper]**](https://arxiv.org/abs/1903.09359) [**[github]**](https://github.com/XgTu/2DASL)
Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning
#### Face attack & Defends
[1] A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing
[2] Deep Tree Learning for Zero-Shot Face Anti-Spoofing
[3] Decorrelated Adversarial Learning for Age-Invariant Face Recognition
[4] Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
[5] Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition
## βοΈ Open source lib
#### face recognition
- [face.evoLVe.](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch)
- [face_recognition.pytorch](https://github.com/grib0ed0v/face_recognition.pytorch)
- [insightface](https://github.com/deepinsight/insightface )
- [face_recognition_framework](https://github.com/XiaohangZhan/face_recognition_framework)#### face detection
- [libfaccedetection](https://github.com/ShiqiYu/libfacedetection)
## π¦ Datasets
#### 2D Face Recognition
| Datasets | Description | Links | Publish Time |
| -------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------ |
| **CASIA-WebFace** | **10,575** subjects and **494,414** images | [Download](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) | 2014 |
| **MegaFace**π | **1 million** faces, **690K** identities | [Download](http://megaface.cs.washington.edu/) | 2016 |
| **MS-Celeb-1M**π | about **10M** images for **100K** celebrities Concrete measurement to evaluate the performance of recognizing one million celebrities | [Download](http://www.msceleb.org) | 2016 |
| **LFW**π | **13,000** images of faces collected from the web. Each face has been labeled with the name of the person pictured. **1680** of the people pictured have two or more distinct photos in the data set. | [Download](http://vis-www.cs.umass.edu/lfw/) | 2007 |
| **VGG Face2**π | The dataset contains **3.31 million** images of **9131** subjects (identities), with an average of 362.6 images for each subject. | [Download](http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) | 2017 |
| **UMDFaces Dataset-image** | **367,888 face annotations** for **8,277 subjects.** | [Download](http://www.umdfaces.io) | 2016 |
| **Trillion Pairs**π | Train: **MS-Celeb-1M-v1c** & **Asian-Celeb** Test: **ELFW&DELFW** | [Download](http://trillionpairs.deepglint.com/overview) | 2018 |
| **FaceScrub** | It comprises a total of **106,863** face images of male and female **530** celebrities, with about **200 images per person**. | [Download](http://vintage.winklerbros.net/facescrub.html) | 2014 |
| **Mut1ny**π | head/face segmentation dataset contains over 17.3k labeled images | [Download](http://www.mut1ny.com/face-headsegmentation-dataset) | 2018 |
| **IMDB-Face** | The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. | [Download](https://github.com/fwang91/IMDb-Face) | 2018 |
| **DiF** | 'Diversity in Faces' Dataset to Advance Study of Fairness in Facial Recognition Systems | [Download](https://www.ibm.com/blogs/research/2019/01/diversity-in-faces/) | 2019 |
| **Megaface2** | Level Playing Field for Million Scale Face Recognition(**672K people in 4.7M images**) | [Download](http://megaface.cs.washington.edu/dataset/download_training.html) | 2019 |#### Video face recognition
| Datasets | Description | Links | Publish Time |
| --------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------ |
| **YouTube Face**π | The data set contains **3,425** videos of **1,595** different people. | [Download](http://www.cs.tau.ac.il/%7Ewolf/ytfaces/) | 2011 |
| **UMDFaces Dataset-video**π | Over **3.7 million** annotated video frames from over **22,000** videos of **3100 subjects.** | [Download](http://www.umdfaces.io) | 2017 |
| **PaSC** | The challenge includes 9,376 still images and 2,802 videos of 293 people. | [Download](https://www.nist.gov/programs-projects/point-and-shoot-face-recognition-challenge-pasc) | 2013 |
| **YTC** | The data consists of two parts: video clips (1910 sequences of 47 subjects) and initialization data(initial frame face bounding boxes, manually marked). | [Download](http://seqamlab.com/youtube-celebrities-face-tracking-and-recognition-dataset/) | 2008 |
| **iQIYI-VID**π | The iQIYI-VID dataset **contains 500,000 videos clips of 5,000 celebrities, adding up to 1000 hours**. This dataset supplies multi-modal cues, including face, cloth, voice, gait, and subtitles, for character identification. | [Download](http://challenge.ai.iqiyi.com/detail?raceId=5b1129e42a360316a898ff4f) | 2018 |#### 3D face recognition
| Datasets | Description | Links | Publish Time |
| -------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------ |
| **Bosphorus**π | 105 subjects and 4666 faces 2D & 3D face data | [Download](http://bosphorus.ee.boun.edu.tr/default.aspx) | 2008 |
| **BD-3DFE** | Analyzing **Facial Expressions** in **3D** Space | [Download](http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html) | 2006 |
| **ND-2006** | 422 subjects and 9443 faces 3D Face Recognition | [Download](https://sites.google.com/a/nd.edu/public-cvrl/data-sets) | 2006 |
| **FRGC V2.0** | 466 subjects and 4007 of 3D Face, Visible Face Images | [Download](https://sites.google.com/a/nd.edu/public-cvrl/data-sets) | 2005 |
| **B3D(AC)^2** | **1000** high quality, dynamic **3D scans** of faces, recorded while pronouncing a set of English sentences. | [Download](http://www.vision.ee.ethz.ch/datasets/b3dac2.en.html) | 2010 |#### Anti-spoofing
| Datasets | \# of subj. / \# of sess. | Links | Year | Spoof attacks attacks | Publish Time |
| ----------------- | :-----------------------: | ------------------------------------------------------------ | ---- | --------------------- | ------------ |
| **NUAA** | 15/3 | [Download](http://parnec.nuaa.edu.cn/xtan/data/nuaaimposterdb.html) | 2010 | **Print** | 2010 |
| **CASIA-MFSD** | 50/3 | Download(link failed) | 2012 | **Print, Replay** | 2012 |
| **Replay-Attack** | 50/1 | [Download](https://www.idiap.ch/dataset/replayattack) | 2012 | **Print, 2 Replay** | 2012 |
| **MSU-MFSD** | 35/1 | [Download](https://www.cse.msu.edu/rgroups/biometrics/Publications/Databases/MSUMobileFaceSpoofing/index.htm) | 2015 | **Print, 2 Replay** | 2015 |
| **MSU-USSA** | 1140/1 | [Download](http://biometrics.cse.msu.edu/Publications/Databases/MSU_USSA/) | 2016 | **2 Print, 6 Replay** | 2016 |
| **Oulu-NPU** | 55/3 | [Download](https://sites.google.com/site/oulunpudatabase/) | 2017 | **2 Print, 6 Replay** | 2017 |
| **Siw** | 165/4 | [Download](http://cvlab.cse.msu.edu/spoof-in-the-wild-siw-face-anti-spoofing-database.html) | 2018 | **2 Print, 4 Replay** | 2018 |#### Cross age and cross pose
| Datasets | Description | Links | Publish Time |
| ------------ | :----------------------------------------------------------- | ------------------------------------------------------------ | ------------ |
| **CACD2000** | The dataset contains more than 160,000 images of 2,000 celebrities with **age ranging from 16 to 62**. | [Download](http://bcsiriuschen.github.io/CARC/) | 2014 |
| **FGNet** | The dataset contains more than 1002 images of 82 people with **age ranging from 0 to 69**. | [Download](http://www-prima.inrialpes.fr/FGnet/html/benchmarks.html) | 2000 |
| **MPRPH** | The MORPH database contains **55,000** images of more than **13,000** people within the age ranges of **16** to **77** | [Download](http://www.faceaginggroup.com/morph/) | 2016 |
| **CPLFW** | we construct a Cross-Pose LFW (CPLFW) which deliberately searches and selects **3,000 positive face pairs** with **pose difference** to add pose variation to intra-class variance. | [Download](http://www.whdeng.cn/cplfw/index.html) | 2017 |
| **CALFW** | Thereby we construct a Cross-Age LFW (CALFW) which deliberately searches and selects **3,000 positive face pairs** with **age gaps** to add aging process intra-class variance. | [Download](http://www.whdeng.cn/calfw/index.html) | 2017 |#### Face Detection
| Datasets | Description | Links | Publish Time |
| ---------------- | ------------------------------------------------------------ | ----------------------------------------------------------- | ------------ |
| **FDDB**π | **5171** faces in a set of **2845** images | [Download](http://vis-www.cs.umass.edu/fddb/index.html) | 2010 |
| **Wider-face** π | **32,203** images and label **393,703** faces with a high degree of variability in scale, pose and occlusion, organized based on **61** event classes | [Download](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) | 2015 |
| **AFW** | AFW dataset is built using Flickr images. It has **205** images with **473** labeled faces. For each face, annotations include a rectangular **bounding box**, **6 landmarks** and the **pose angles**. | [Download](http://www.ics.uci.edu/~xzhu/face/) | 2013 |
| **MALF** | MALF is the first face detection dataset that supports fine-gained evaluation. MALF consists of **5,250** images and **11,931** faces. | [Download](http://www.cbsr.ia.ac.cn/faceevaluation/) | 2015 |#### Face Attributes
| Datasets | Description | Links | Key features | Publish Time |
| ------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | -------------------------------------------- | ------------ |
| **CelebA** | **10,177** number of **identities**, **202,599** number of **face images**, and **5 landmark locations**, **40 binary attributes** annotations per image. | [Download](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | **attribute & landmark** | 2015 |
| **IMDB-WIKI** | 500k+ face images with **age** and **gender** labels | [Download](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/) | **age & gender** | 2015 |
| **Adience** | Unfiltered faces for **gender** and **age** classification | [Download](http://www.openu.ac.il/home/hassner/Adience/data.html) | **age & gender** | 2014 |
| **WFLW**π | WFLW contains **10000 faces** (7500 for training and 2500 for testing) with **98 fully manual annotated landmarks**. | [Download](https://wywu.github.io/projects/LAB/WFLW.html) | **landmarks** | 2018 |
| **Caltech10k Web Faces** | The dataset has 10,524 human faces of various resolutions and in **different settings** | [Download](http://www.vision.caltech.edu/Image_Datasets/Caltech_10K_WebFaces/#Description) | **landmarks** | 2005 |
| **EmotioNet** | The EmotioNet database includes**950,000 images** with **annotated AUs**. A **subset** of the images in the EmotioNet database correspond to **basic and compound emotions.** | [Download](http://cbcsl.ece.ohio-state.edu/EmotionNetChallenge/index.html#overview) | **AU and Emotion** | 2017 |
| **RAF( Real-world Affective Faces)** | **29672** number of **real-world images**, including **7** classes of basic emotions and **12** classes of compound emotions, **5 accurate landmark locations**, **37 automatic landmark locations**, **race, age range** and **gender** **attributes** annotations per image | [Download]( ) | **Emotionsγlandmarkγraceγage and gender** | 2017 |
| **FairFace** | FairFace: Face Attribute Dataset for **Balanced Race**, **Gender**, and **Age** | | **balance race compoition** | 2019 |
| **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 | [Download](https://adrianbulat.com/face-alignment) | **3D landmark** | 2017 |#### Others
| Datasets | Description | Links | Publish Time |
| ------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------ |
| **IJB C/B/A**π | IJB C/B/A is currently running **three challenges** related to **face detection, verification, identification, and identity clustering.** | [Download](https://www.nist.gov/programs-projects/face-challenges) | 2015 |
| **MOBIO** | **bi-modal** (**audio** and **video**) data taken from 152 people. | [Download](https://www.idiap.ch/dataset/mobio) | 2012 |
| **BANCA** | The BANCA database was captured in four European languages in **two modalities** (**face** and **voice**). | [Download](http://www.ee.surrey.ac.uk/CVSSP/banca/) | 2014 |
| **3D Mask Attack** | **76500** frames of **17** persons using Kinect RGBD with eye positions (Sebastien Marcel). | [Download](https://www.idiap.ch/dataset/3dmad) | 2013 |
| **WebCaricature** | **6042** **caricatures** and **5974 photographs** from **252 persons** collected from the web | [Download](https://cs.nju.edu.cn/rl/WebCaricature.htm) | 2018 |## π Research home(conf & workshop & trans)
![](./img/research_home.jpg)
###### Conference
**ICCV**: [IEEE International Conference on Computer Vision](http://iccv2019.thecvf.com)
**CVPR**: [IEEE Conference on Computer Vision and Pattern Recognition](http://cvpr2018.thecvf.com/)
**ECCV**: [European Conference on Computer Vision](https://eccv2018.org)
**FG**: [IEEE International Conference on Automatic Face and Gesture Recognition](http://dblp.uni-trier.de/db/conf/fgr/)
**BMVC:** [The British Machine Vision Conference](http://www.bmva.org/bmvc/?id=bmvc)
**IJCB[ICB+BTAS]**:International Joint Conference on Biometrics
- **ICB**: [International Conference on Biometrics](http://icb2018.org)
- **BTAS**: [IEEE International Conference on Biometrics: Theory, Applications and Systems]()
**AMFG**: IEEE workshop on Analysis and Modeling of Faces and Gestures
###### Workshop on Biometrics
**TPAMI:** [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34)
**IJCV:** [International Journal of Computer Vision](https://link.springer.com/journal/11263)
**TIP:** [IEEE Transactions on Image Processing](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83)
**TIFS:** [IEEE Transactions on Information Forensics and Security](IEEE Transactions on Information Forensics and Security)
**PR:** [Pattern Recognition](https://www.journals.elsevier.com/pattern-recognition/)
## π· References
[1]
[2]
[3] https://github.com/betars/Face-Resources
[4] https://zhuanlan.zhihu.com/p/33288325
[5] https://github.com/L706077/DNN-Face-Recognition-Papers
[6] https://www.zhihu.com/question/67919300
[7] https://jackietseng.github.io/conference_call_for_paper/2018-2019-conferences.html
[8]http://history.ccf.org.cn/sites/ccf/biaodan.jsp?contentId=2903940690839
[9]http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html