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πŸ”₯ face releated algorithm, datasets and papers

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πŸ“ Paper / Algorithm

Survey

2D- Face Recognition

2d_face_reg

[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

[11] NormFace [paper] [code]

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)

[16] ArcFace: [paper] [code]

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

cos_loss

Face Detection

[1] Cascade CNN [paper] [code]

A Convolutional Neural Network Cascade for Face Detection

[2] MTCNN [Paper] [code]

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

[6] SSH [paper] [code]

SSH: Single Stage Headless Face Detector

[7] S3FD [paper]

Single Shot Scale-invariant Face Detector

[8] FaceBoxes [paper] [code]

Faceboxes: A CPU Real-time Face Detector with High Accuracy

[9] Scaleface [paper]

Face Detection through Scale-Friendly Deep Convolutional Networks

[10] HR [paper] [code]

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

[1] PRNet [paper] [code]

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

[2]LAB Paper [code]

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

[2] 3DDFA [paper] [github]

Face Alignment in Full Pose Range: A 3D Total Solution.

[3] VRN [index] [code]

Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression(3D Face Reconstruction from a Single Image)

[4] PRNet [paper] [github]

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

[5] 2DASL [paper] [github]

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

βš™οΈ Open source lib

face recognition

face detection

πŸ“¦ Datasets

2D Face Recognition

DatasetsDescriptionLinksPublish Time
CASIA-WebFace10,575 subjects and 494,414 imagesDownload2014
MegaFaceπŸ…1 million faces, 690K identitiesDownload2016
MS-Celeb-1MπŸ…about 10M images for 100K celebrities Concrete measurement to evaluate the performance of recognizing one million celebritiesDownload2016
LFWπŸ…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.Download2007
VGG Face2πŸ…The dataset contains 3.31 million images of 9131 subjects (identities), with an average of 362.6 images for each subject.Download2017
UMDFaces Dataset-image367,888 face annotations for 8,277 subjects.Download2016
Trillion PairsπŸ…Train: MS-Celeb-1M-v1c & Asian-Celeb Test: ELFW&DELFWDownload2018
FaceScrubIt comprises a total of 106,863 face images of male and female 530 celebrities, with about 200 images per person.Download2014
Mut1nyπŸ…head/face segmentation dataset contains over 17.3k labeled imagesDownload2018
IMDB-FaceThe dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images.Download2018
DiF'Diversity in Faces' Dataset to Advance Study of Fairness in Facial Recognition SystemsDownload2019
Megaface2Level Playing Field for Million Scale Face Recognition(672K people in 4.7M images)Download2019

Video face recognition

DatasetsDescriptionLinksPublish Time
YouTube FaceπŸ…The data set contains 3,425 videos of 1,595 different people.Download2011
UMDFaces Dataset-videoπŸ…Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.Download2017
PaSCThe challenge includes 9,376 still images and 2,802 videos of 293 people.Download2013
YTCThe data consists of two parts: video clips (1910 sequences of 47 subjects) and initialization data(initial frame face bounding boxes, manually marked).Download2008
iQIYI-VIDπŸ…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.Download2018

3D face recognition

DatasetsDescriptionLinksPublish Time
BosphorusπŸ…105 subjects and 4666 faces 2D & 3D face dataDownload2008
BD-3DFEAnalyzing Facial Expressions in 3D SpaceDownload2006
ND-2006422 subjects and 9443 faces 3D Face RecognitionDownload2006
FRGC V2.0466 subjects and 4007 of 3D Face, Visible Face ImagesDownload2005
B3D(AC)^21000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences.Download2010

Anti-spoofing

Datasets# of subj. / # of sess.LinksYearSpoof attacks attacksPublish Time
NUAA15/3Download2010Print2010
CASIA-MFSD50/3Download(link failed)2012Print, Replay2012
Replay-Attack50/1Download2012Print, 2 Replay2012
MSU-MFSD35/1Download2015Print, 2 Replay2015
MSU-USSA1140/1Download20162 Print, 6 Replay2016
Oulu-NPU55/3Download20172 Print, 6 Replay2017
Siw165/4Download20182 Print, 4 Replay2018

Cross age and cross pose

DatasetsDescriptionLinksPublish Time
CACD2000The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16 to 62.Download2014
FGNetThe dataset contains more than 1002 images of 82 people with age ranging from 0 to 69.Download2000
MPRPHThe MORPH database contains 55,000 images of more than 13,000 people within the age ranges of 16 to 77Download2016
CPLFWwe 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.Download2017
CALFWThereby 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.Download2017

Face Detection

DatasetsDescriptionLinksPublish Time
FDDBπŸ…5171 faces in a set of 2845 imagesDownload2010
Wider-face πŸ…32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion, organized based on 61 event classesDownload2015
AFWAFW 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.Download2013
MALFMALF is the first face detection dataset that supports fine-gained evaluation. MALF consists of 5,250 images and 11,931 faces.Download2015

Face Attributes

DatasetsDescriptionLinksKey featuresPublish Time
CelebA10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.Downloadattribute & landmark2015
IMDB-WIKI500k+ face images with age and gender labelsDownloadage & gender2015
AdienceUnfiltered faces for gender and age classificationDownloadage & gender2014
WFLWπŸ…WFLW contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks.Downloadlandmarks2018
Caltech10k Web FacesThe dataset has 10,524 human faces of various resolutions and in different settingsDownloadlandmarks2005
EmotioNetThe EmotioNet database includes950,000 images with annotated AUs. A subset of the images in the EmotioNet database correspond to basic and compound emotions.DownloadAU and Emotion2017
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 imageDownloadEmotions、landmark、race、age and gender2017
FairFaceFairFace: Face Attribute Dataset for Balanced Race, Gender, and Agebalance race compoition2019
LS3D-WA 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 methodDownload3D landmark2017

Others

DatasetsDescriptionLinksPublish Time
IJB C/B/AπŸ…IJB C/B/A is currently running three challenges related to face detection, verification, identification, and identity clustering.Download2015
MOBIObi-modal (audio and video) data taken from 152 people.Download2012
BANCAThe BANCA database was captured in four European languages in two modalities (face and voice).Download2014
3D Mask Attack76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel).Download2013
WebCaricature6042 caricatures and 5974 photographs from 252 persons collected from the webDownload2018

🏠 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)

PR: Pattern Recognition

🏷 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