Single Sample Face Recognition via Learning Deep Supervised Autoencoders

@article{Gao2015SingleSF,
  title={Single Sample Face Recognition via Learning Deep Supervised Autoencoders},
  author={Shenghua Gao and Yuting Zhang and Kui Jia and Jiwen Lu and Yingying Zhang},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2015},
  volume={10},
  pages={2108-2118}
}
This paper targets learning robust image representation for single training sample per person face recognition. Motivated by the success of deep learning in image representation, we propose a supervised autoencoder, which is a new type of building block for deep architectures. There are two features distinct our supervised autoencoder from standard autoencoder. First, we enforce the faces with variants to be mapped with the canonical face of the person, for example, frontal face with neutral… CONTINUE READING
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