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Deep Learning for Face Recognition

Deep Learning for Face Recognition (May 2016)

Popular architectures

  • FaceNet (Google)
    • They use a triplet loss with the goal of keeping the L2 intra-class distances low and inter-class distances high
  • DeepID (Hong Kong University)
    • They use verification and identification signals to train the network. Afer each convolutional layer there is an identity layer connected to the supervisory signals in order to train each layer closely (on top of normal backprop)
  • DeepFace (Facebook)
    • Convs followed by locally connected, followed by fully connected

Face databases

Face Recognition

Face alignment

Fast face recognition and verification

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