face releated algorithm, datasets and papers
📝 Paper / Algorithm
Survey
- Deep Face Recognition: A Survey paper
- Face Recognition: From Traditional to Deep Learning Methods paper
- Deep Facial Expression Recognition: A Survey paper
- A Survey on Face Detection and Classification for Partially Occluded images paper
- 3D face recognition: a survey paper
- Face detection techniques: a review paper
2D- Face Recognition
[1] DeepID1 [paper]
Deep Learning Face Representation from Predicting 10,000 Classes
[2] DeepID2 [paper]
Deep Learning Face Representation by Joint Identification-Verification
[3] DeepID2+ [paper]
Deeply learned face representations are sparse, selective, and robust
[4] DeepIDv3 [paper]
DeepID3: Face Recognition with Very Deep Neural Networks
[5] Deep Face [paper]
Deep Face Recognition
[6] Center Loss [paper] [code]
A Discriminative Feature Learning Approach for Deep Face Recognition
[7]Marginal loss [paper]
Marginal loss for deep face recognition
[8] Range Loss[paper]
Range Loss for Deep Face Recognition with Long-tail
[9]Contrastive Loss [paper]
Deep learning face representation by joint identification-verification
[10] FaceNet [paper] [third-party implemention]
FaceNet: A Unified Embedding for Face Recognition and Clustering
NormFace: L2 Hypersphere Embedding for Face Verification
[12] COCO Loss: [paper] [code]
Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
[13] Large-Margin Softmax Loss [paper] [code]
Large-Margin Softmax Loss for Convolutional Neural Networks(L-Softmax loss)
[14]SphereFace: A-Softmax [paper] [code]
SphereFace: Deep Hypersphere Embedding for Face Recognition
[15]AM-Softmax/cosFace [paper AM-Softmax] [paper cosFace] [AM-softmax code]
AM : Additive Margin Softmax for Face Verification
CosFace: Large Margin Cosine Loss for Deep Face Recognition(Tencent AI Lab)
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
[17] Adaptive Face paper
Adaptive Margin and Sampling for Face Recognition
[18] AdaCos Paper
Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
[20] RegularFace: paper
Deep Face Recognition via Exclusive Regularization
[21] UniformFace: paper
Learning Deep Equidistributed Representation for Face Recognition
[22] P2SGrad: paper
Refined Gradients for Optimizing Deep Face Models
Face Detection
[1] Cascade CNN [paper] [code]
A Convolutional Neural Network Cascade for Face Detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
[3] ICC - CNN [paper]
Detecting Faces Using Inside Cascaded Contextual CNN
[4] Face R-CNN [Paper]
Face R-CNN
[5] Deep-IR[Paper]
Face Detection using Deep Learning: An Improved Faster RCNN Approach
SSH: Single Stage Headless Face Detector
[7] S3FD [paper]
Single Shot Scale-invariant Face Detector
Faceboxes: A CPU Real-time Face Detector with High Accuracy
[9] Scaleface [paper]
Face Detection through Scale-Friendly Deep Convolutional Networks
Finding Tiny Faces
[11] FAN [paper]
Feature Agglomeration Networks for Single Stage Face Detection.
[12] PyramidBox [paper] [code]
PyramidBox: A Context-assisted Single Shot Face Detector
[13] SRN [paper]
Selective Refinement Network for High Performance Face Detection.
[14] DSFD [paper]
DSFD: Dual Shot Face Detector
[15] VIM FD [paper]
Robust and High Performance Face Detector
[16] ISRN [paper]
Improved Selective Refinement Network for Face Detection
[17] PyramidBox++ [Paper]
PyramidBox++: High Performance Detector for Finding Tiny Face
[18] RetinaFace [paper] [code]
RetinaFace: Single-stage Dense Face Localisation in the Wild
Face Alignment
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
[3]PFLD Paper [demo code]
PFLD: A Practical Facial Landmark Detector
[4] 2D & 3D FAN [Paper] [code]
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
Face Alignment in Full Pose Range: A 3D Total Solution.
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression(3D Face Reconstruction from a Single Image)
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
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
⚙️ Open source lib
face recognition
face detection
📦 Datasets
2D Face Recognition
Datasets | Description | Links | Publish Time |
---|---|---|---|
CASIA-WebFace | 10,575 subjects and 494,414 images | Download | 2014 |
MegaFace🏅 | 1 million faces, 690K identities | Download | 2016 |
MS-Celeb-1M🏅 | about 10M images for 100K celebrities Concrete measurement to evaluate the performance of recognizing one million celebrities | Download | 2016 |
LFW🏅 | 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 Face2🏅 | 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 Pairs🏅 | 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 |
Mut1ny🏅 | 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 |
DiF | 'Diversity in Faces' Dataset to Advance Study of Fairness in Facial Recognition Systems | Download | 2019 |
Megaface2 | Level Playing Field for Million Scale Face Recognition(672K people in 4.7M images) | Download | 2019 |
Video face recognition
Datasets | Description | Links | Publish Time |
---|---|---|---|
YouTube Face🏅 | The data set contains 3,425 videos of 1,595 different people. | Download | 2011 |
UMDFaces Dataset-video🏅 | 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-VID🏅 | 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 |
3D face recognition
Datasets | Description | Links | Publish Time |
---|---|---|---|
Bosphorus🏅 | 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 |
Anti-spoofing
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 |
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 | 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 |
Face Detection
Datasets | Description | Links | Publish Time |
---|---|---|---|
FDDB🏅 | 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 |
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 | 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 |
WFLW🏅 | 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 | Emotions、landmark、race、age 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 | 3D landmark | 2017 |
Others
Datasets | Description | Links | Publish Time |
---|---|---|---|
IJB C/B/A🏅 | 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 |
🏠 Research home(conf & workshop & trans)
Conference
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
AMFG: IEEE workshop on Analysis and Modeling of Faces and Gestures
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)
🏷 References
[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
https://github.com/polarisZhao/awesome-face
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