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


  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

  9. A Discriminative Feature Learning Approach for Deep Face Recognition

  10. NormFace: L2 Hypersphere Embedding for Face Verification.* attention: model released !*

  11. SphereFace: Deep Hypersphere Embedding for Face Recognition


  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. 10.3D Mask Attack Dataset. 76500 frames of 17 persons using Kinect RGBD with eye positions (Sebastien Marcel)
  10. Audio-visual database for face and speaker recognition.Mobile Biometry MOBIO
  11. BANCA face and voice database. Univ of Surrey
  12. Binghampton Univ 3D static and dynamic facial expression database. (Lijun Yin, Peter Gerhardstein and teammates)
  13. The BioID Face Database. BioID group
  14. Biwi 3D Audiovisual Corpus of Affective Communication. 1000 high quality, dynamic 3D scans of faces, recorded while pronouncing a set of English sentences.
  15. Cohn-Kanade AU-Coded Expression Database. 500+ expression sequences of 100+ subjects, coded by activated Action Units (Affect Analysis Group, Univ. of Pittsburgh.
  16. CMU/MIT Frontal Faces . Training set: 2,429 faces, 4,548 non-faces; Test set: 472 faces, 23,573 non-faces.
  17. AT&T Database of Faces 400 faces of 40 people (10 images per people)

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.

  3. SeetaFace Engine. SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence.

  4. Caffe-face - Caffe Face is developed for face recognition using deep neural networks.

  5. Norm-Face - Norm Face, finetuned from center-face and Light-CNN


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


  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
  4. libfacedetection. A binary library for face detection in images.
  5. SeetaFaceEngine. An open source C++ face recognition engine.


  1. Caffe
  2. Torch7
  3. Theano
  4. cuda-convnet
  5. MXNET
  6. Tensorflow
  7. tiny-dnn


  1. faceswap Face swapping with Python, dlib, and OpenCV
  2. Facial Keypoints Detection Competition on Kaggle.
  3. An implementation of Face Alignment at 3000fps via Local Binary Features

Created by betars on 27/10/2015.