Following is a growing list of some of the materials I found on the web for research on face recognition algorithm.
##Papers
- DeepFace.A work from Facebook.
- FaceNet.A work from Google.
- One Millisecond Face Alignment with an Ensemble of Regression Trees. Dlib implements the algorithm.
- DeepID
- DeepID2
- DeepID3
- Learning Face Representation from Scratch
- Face Search at Scale: 80 Million Gallery
##Datasets
- 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) Dataset 202,599 images and 10,177 subjects. 5 landmark locations, 40 binary attributes.
- MSRA-CFW. 202,792 images and 1,583 subjects.
- MegaFace Dataset 1 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.
##Trained Model
- openface. Face recognition with Google's FaceNet deep neural network using Torch.
- VGG-Face. VGG-Face CNN descriptor. Impressed embedding loss.
##Tutorial
- Deep Learning for Face Recognition. Shiguan Shan, Xiaogang Wang, and Ming yang.
##Software
- 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
##Frameworks
##Miscellaneous
- faceswap Face swapping with Python, dlib, and OpenCV
- Facial Keypoints Detection Competition on Kaggle.
Created by betars on 27/10/2015.