๐ face releated algorithm, datasets and papers ๐
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Deep ID Series
DeepID1: Deep Learning Face Representation from Predicting 10,000 Classes [Yi Sun et al., 2014]
DeepID2: Deep Learning Face Representation by Joint Identification-Verification [Yi Sun et al., 2014]
DeepID2+: Deeply learned face representations are sparse, selective, and robust [Yi Sun et al., 2014]
DeepIDv3: DeepID3: Face Recognition with Very Deep Neural Networks [Yi Sun et al., 2015]
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FaceNet: [third-party implemention]
FaceNet: A Unified Embedding for Face Recognition and Clustering [Florian Schroff et al., 2015]
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Deep Face:
Deep Face Recognition [Omkar M. Parkhi et al., 2015]
- Center Loss๏ผ [code] A Discriminative Feature Learning Approach for Deep Face Recognition [Yandong Wen et al., 2016]
- Large-Margin Softmax Loss [code] Large-Margin Softmax Loss for Convolutional Neural Networks(L-Softmax loss) [Weiyang Liu al., 2017]
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SphereFace๏ผ A-Softmax [code] SphereFace: Deep Hypersphere Embedding for Face Recognition [Weiyang Liu al., 2017]
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NormFace [code]
NormFace: L2 Hypersphere Embedding for Face Verification [Feng Wang al., 2017]
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COCO Loss: [code]
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition [Yu Liu al., 2017]
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AM-Softmax [code] AM : Additive Margin Softmax for Face Verification [Feng Wang al., 2018]
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CosFace:
CosFace: Large Margin Cosine Loss for Deep Face Recognition(Tencent AI Lab) [Hao Wang al., 2018]
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ArcFace: [code]
ArcFace: Additive Angular Margin Loss for Deep Face Recognition [Jiankang Deng al., 2018]
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CCL๏ผ
Face Recognition via Centralized Coordinate Learning [Xianbiao al., 2018]
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Deep 3D Face Identification [Donghyun Kim al., 2017]
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Learning from Millions of 3D Scans for Large-scale 3D Face Recognition[Syed Zulqarnain al., 2018]
- Beyond triplet loss: a deep quadruplet network for person re-identification[Weihua Chen al., 2017]
- Range Loss for Deep Face Recognition with Long-tail [Xiao Zhang al., 2016]
- Feature Incay for Representation Regularization[Yuhui Yuan al., 2017]
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Cascade [code]
A Convolutional Neural Network Cascade for Face Detection[Haoxiang Li al., 2015]
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MTCNN [code]
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks[K. Zhang al., 2016]
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ICC - CNN
- Face R-CNN
โ Face R-CNN[H. Wang, Z. Li, X. Ji, Y. Wang. et.al 2017]
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CMS-RCNN
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection[Chenchen Zhu et.al 2016]
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Deep-IR
Face Detection using Deep Learning: An Improved Faster RCNN Approach[Xudong SUN et.al 2017]
- SSH [code]
โ SSH: Single Stage Headless Face Detector[M. Najibi, al., 2017ย ]
- s3fd
โ Single Shot Scale-invariant Face Detector[Zhang, Shifeng al., 2017]
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FaceBoxes code
Faceboxes: A CPU Real-time Face Detector with High Accuracy[Zhang, Shifeng al., 2017]
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Scaleface
Face Detection through Scale-Friendly Deep Convolutional Networks[Shuo Yang al., 2017]
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HR [code]
Finding Tiny Faces [Peiyun Hu,ย Deva Ramanan, 2017]
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PyramidBox
PyramidBox: A Context-assisted Single Shot Face Detector[Xu Tang al., 2018]
- Look at Boundary: A Boundary-Aware Face Alignment Algorithm[Wayne Wu al., 2018]
- PFLD: A Practical Facial Landmark Detector[Xiaojie Guo al., 2019]
- Exploring Disentangled Feature Representation Beyond Face Identification[Yu Liu al., 2018]
1. face.evoLVe. github
2. libfaccedetection github
| Datasets | Description | Links | Publish Time |
|---|---|---|---|
| CASIA-WebFace | 10,575 subjects and 494,414 images | Download | 2014 |
| MegaFace๐ | 1 million faces, 690K identities | Download | 2016 |
| MS-Celeb-1M๐ | about 10M images for 100K celebrities Concrete measurement to evaluate the performance of recognizing one million celebrities | Download | 2016 |
| LFW๐ | 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 | 2007 |
| VGG Face2๐ | The dataset contains 3.31 million images of 9131 subjects (identities), with an average of 362.6 images for each subject. | Download | 2017 |
| UMDFaces Dataset-image | 367,888 face annotations for 8,277 subjects. | Download | 2016 |
| Trillion Pairs๐ | Train: MS-Celeb-1M-v1c & Asian-Celeb Test: ELFW&DELFW | Download | 2018 |
| FaceScrub | It comprises a total of 106,863 face images of male and female 530 celebrities, with about 200 images per person. | Download | 2014 |
| Mut1ny๐ | head/face segmentation dataset contains over 17.3k labeled images | Download | 2018 |
| IMDB-Face | The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. | Download | 2018 |
| Datasets | Description | Links | Publish Time |
|---|---|---|---|
| YouTube Face๐ | The data set contains 3,425 videos of 1,595 different people. | Download | 2011 |
| UMDFaces Dataset-video๐ | Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects. | Download | 2017 |
| PaSC | The challenge includes 9,376 still images and 2,802 videos of 293 people. | Download | 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 | 2008 |
| iQIYI-VID๐ | 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 | 2018 |
| Datasets | Description | Links | Publish Time |
|---|---|---|---|
| Bosphorus๐ | 105 subjects and 4666 faces 2D & 3D face data | Download | 2008 |
| BD-3DFE | Analyzing Facial Expressions in 3D Space | Download | 2006 |
| ND-2006 | 422 subjects and 9443 faces 3D Face Recognition | Download | 2006 |
| FRGC V2.0 | 466 subjects and 4007 of 3D Face, Visible Face Images | Download | 2005 |
| B3D(AC)^2 | 1000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences. | Download | 2010 |
| Datasets | # of subj. / # of sess. | Links | Year | Spoof attacks attacks | Publish Time |
|---|---|---|---|---|---|
| NUAA | 15/3 | Download | 2010 | 2010 | |
| CASIA-MFSD | 50/3 | Download(link failed) | 2012 | Print, Replay | 2012 |
| Replay-Attack | 50/1 | Download | 2012 | Print, 2 Replay | 2012 |
| MSU-MFSD | 35/1 | Download | 2015 | Print, 2 Replay | 2015 |
| MSU-USSA | 1140/1 | Download | 2016 | 2 Print, 6 Replay | 2016 |
| Oulu-NPU | 55/3 | Download | 2017 | 2 Print, 6 Replay | 2017 |
| Siw | 165/4 | Download | 2018 | 2 Print, 4 Replay | 2018 |
| 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 | 2014 |
| FGNet | The dataset contains more than 1002 images of 82 people with age ranging from 0 to 69. | Download | 2000 |
| MPRPH | The MORPH database contains 55,000 images of more than 13,000 people within the age ranges of 16 to 77 | Download | 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 | 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 | 2017 |
| Datasets | Description | Links | Publish Time |
|---|---|---|---|
| FDDB๐ | 5171 faces in a set of 2845 images | Download | 2010 |
| Wider-face | 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 | 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 | 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 | 2015 |
| 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 | attribute & landmark | 2015 |
| IMDB-WIKI | 500k+ face images with age and gender labels | Download | age & gender | 2015 |
| Adience | Unfiltered faces for gender and age classification | Download | age & gender | 2014 |
| WFLW๐ | WFLW contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks. | Download | landmarks | 2018 |
| Caltech10k Web Faces | The dataset has 10,524 human faces of various resolutions and in different settings | Download | landmarks | 2005 |
| EmotioNet | The EmotioNet database includes950,000 images with annotated AUs. A subset of the images in the EmotioNet database correspond to basic and compound emotions. | Download | 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 | Emotionsใlandmarkใraceใage and gender | 2017 |
| Datasets | Description | Links | Publish Time |
|---|---|---|---|
| IJB C/B/A๐ | IJB C/B/A is currently running three challenges related to face detection, verification, identification, and identity clustering. | Download | 2015 |
| MOBIO | bi-modal (audio and video) data taken from 152 people. | Download | 2012 |
| BANCA | The BANCA database was captured in four European languages in two modalities (face and voice). | Download | 2014 |
| 3D Mask Attack | 76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel). | Download | 2013 |
| WebCaricature | 6042 caricatures and 5974 photographs from 252 persons collected from the web | Download | 2018 |
ICCV: IEEE International Conference on Computer Vision
CVPR: IEEE Conference on Computer Vision and Pattern Recognition
ECCV: European Conference on Computer Vision
FG: IEEE International Conference on Automatic Face and Gesture Recognition
BMVC: The British Machine Vision Conference
IJCB[ICB+BTAS]:International Joint Conference on Biometrics
ICB: International Conference on Biometrics
BTAS: IEEE International Conference on Biometrics: Theory, Applications and Systems
AMFG: IEEE workshop on Analysis and Modeling of Faces and Gestures
CVPR Workshop on Biometrics
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCV: International Journal of Computer Vision
TIP: IEEE Transactions on Image Processing
TIFS: [IEEE Transactions on Information Forensics and Security](IEEE Transactions on Information Forensics and Security)
[1] https://github.com/RiweiChen/DeepFace/tree/master/FaceDataset
[2] https://www.zhihu.com/question/33505655?sort=created
[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