https://github.com/betars/Face-Resources
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Face-Resources
Following is a growing list of some of the materials I found on the web for research on face recognition algorithm.
DeepFace.A work from Facebook.
FaceNet.A work from Google.
One Millisecond Face Alignment with an Ensemble of Regression Trees. Dlib implements the algorithm.
Learning Face Representation from Scratch
Face Search at Scale: 80 Million Gallery
A Discriminative Feature Learning Approach for Deep Face Recognition
NormFace: L2 Hypersphere Embedding for Face Verification.* attention: model released !*
SphereFace: Deep Hypersphere Embedding for Face Recognition
CASIA WebFace Database. 10,575 subjects and 494,414 images
Labeled Faces in the Wild.13,000 images and 5749 subjects
Large-scale CelebFaces Attributes (CelebA) Dataset202,599 images and 10,177 subjects. 5 landmark locations, 40 binary attributes.
MSRA-CFW. 202,792 images and 1,583 subjects.
MegaFace Dataset1 Million Faces for Recognition at Scale 690,572 unique people
FaceScrub. A Dataset With Over 100,000 Face Images of 530 People.
FDDB.Face Detection and Data Set Benchmark. 5k images.
AFLW.Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. 25k images.
AFW. Annotated Faces in the Wild. ~1k images. 10.3D Mask Attack Dataset. 76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel)
Audio-visual database for face and speaker recognition.Mobile Biometry MOBIOhttp://www.mobioproject.org/
BANCA face and voice database. Univ of Surrey
Binghampton Univ 3D static and dynamic facial expression database. (Lijun Yin, Peter Gerhardstein and teammates)
The BioID Face Database. BioID group
Biwi 3D Audiovisual Corpus of Affective Communication. 1000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences.
Cohn-Kanade AU-Coded Expression Database. 500+ expression sequences of 100+ subjects, coded by activated Action Units (Affect Analysis Group, Univ. of Pittsburgh.
CMU/MIT Frontal Faces. Training set: 2,429 faces, 4,548 non-faces; Test set: 472 faces, 23,573 non-faces.
AT&T Database of Faces400 faces of 40 people (10 images per people)
openface. Face recognition with Google's FaceNet deep neural network using Torch.
VGG-Face. VGG-Face CNN descriptor. Impressed embedding loss.
SeetaFace Engine. SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence.
Caffe-face- Caffe Face is developed for face recognition using deep neural networks.
Norm-Face- Norm Face, finetuned fromcenter-faceandLight-CNN
Deep Learning for Face Recognition. Shiguan Shan, Xiaogang Wang, and Ming yang.
OpenCV. With some trained face detector models.
dlib. Dlib implements a state-of-the-art of face Alignment algorithm.
ccv. With a state-of-the-art frontal face detector
libfacedetection. A binary library for face detection in images.
SeetaFaceEngine. An open source C++ face recognition engine.
##Frameworks
faceswapFace swapping with Python, dlib, and OpenCV
Facial Keypoints DetectionCompetition on Kaggle.
An implementation of Face Alignment at 3000fps via Local Binary Features
