/Face-Resources

Face-Resources

Following is a growing list of some of the materials I found on the web for research on face recognition algorithm.

##Papers

  1. DeepFace.A work from Facebook.
  2. FaceNet.A work from Google.
  3. One Millisecond Face Alignment with an Ensemble of Regression Trees. Dlib implements the algorithm.
  4. DeepID
  5. DeepID2
  6. DeepID3
  7. Learning Face Representation from Scratch
  8. Face Search at Scale: 80 Million Gallery

##Datasets

  1. CASIA WebFace Database. 10,575 subjects and 494,414 images
  2. Labeled Faces in the Wild.13,000 images and 5749 subjects
  3. Large-scale CelebFaces Attributes (CelebA) Dataset 202,599 images and 10,177 subjects. 5 landmark locations, 40 binary attributes.
  4. MSRA-CFW. 202,792 images and 1,583 subjects.
  5. MegaFace Dataset 1 Million Faces for Recognition at Scale 690,572 unique people
  6. FaceScrub. A Dataset With Over 100,000 Face Images of 530 People.
  7. FDDB.Face Detection and Data Set Benchmark. 5k images.
  8. AFLW.Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization. 25k images.
  9. AFW. Annotated Faces in the Wild. ~1k images.

##Trained Model

  1. openface. Face recognition with Google's FaceNet deep neural network using Torch.
  2. VGG-Face. VGG-Face CNN descriptor. Impressed embedding loss.

##Tutorial

  1. Deep Learning for Face Recognition. Shiguan Shan, Xiaogang Wang, and Ming yang.

##Software

  1. OpenCV. With some trained face detector models.
  2. dlib. Dlib implements a state-of-the-art of face Alignment algorithm.
  3. ccv. With a state-of-the-art frontal face detector

##Frameworks

  1. Caffe
  2. Torch7
  3. Theano
  4. cuda-convnet

##Miscellaneous

  1. faceswap Face swapping with Python, dlib, and OpenCV
  2. Facial Keypoints Detection Competition on Kaggle.

Created by betars on 27/10/2015.