Face Verification and Recognition

Framework:

The similarity between two faces Ia and Ib can be unified in the following formulation:

M[W(F(S(Ia))), W(F(S(Ib)))]

in which S is synthesis operation (e.g., face alignment, frontalization), F is robust feature extraction, W is transformation subspace learning, M means face matching algorithm (e.g., NN, SVM, metric learning).

Paper:

• DeepID 1,2,3: Deep learning face representation from predicting 10,000 classes

• FaceNet: A Unified Embedding for Face Recognition and Clustering
  code: https://cmusatyalab.github.io/openface/ (triplet loss)

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification (3D face alignment)

• A Discriminative Feature Learning Approach for Deep Face Recognition
  code: https://github.com/ydwen/caffe-face

• Unconstrained Face Verification using Deep CNN Features (Joint Bayesian Metric Learning)
  code: https://github.com/happynear/FaceVerification

• A Light CNN for Deep Face Representation with Noisy Label
  code: https://github.com/AlfredXiangWu/face_verification_experiment

Survey:

• Face Recognition: From Traditional to Deep Learning Methods

Dataset:

LFW: http://vis-www.cs.umass.edu/lfw/

IJB-A: (free upon request) https://www.nist.gov/itl/iad/image-group/ijba-dataset-request-form

FERET: (free upon request) https://www.nist.gov/itl/iad/image-group/color-feret-database

CMU Multi-Pie: (not free) http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html

CASIA WebFace Database: (free upon request) http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html

MS-Celeb-1M: https://www.microsoft.com/en-us/research/project/ms-celeb-1m-challenge-recognizing-one-million-celebrities-real-world/

MegaFace: (free upon request) http://megaface.cs.washington.edu/dataset/download_training.html

Cross-Age Celebrity Dataset: http://bcsiriuschen.github.io/CARC/

VGG face: http://www.robots.ox.ac.uk/~vgg/data/vgg_face/