Learning Multi-channel Deep Feature Representations for Face Recognition


Xue-wen Chen, Melih Aslan, Kunlei Zhang, Thomas Huang ;
Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, PMLR 44:60-71, 2015.


Deep learning provides a natural way to obtain feature representations from data without relying on hand-crafted descriptors. In this paper, we propose to learn deep feature representations using unsupervised and supervised learning in a cascaded fashion to produce generically descriptive yet class specific features. The proposed method can take full advantage of the availability of large-scale unlabeled data and learn discriminative features (supervised) from generic features (unsupervised). It is then applied to multiple essential facial regions to obtain multi-channel deep facial representations for face recognition. The efficacy of the proposed feature representations is validated on both controlled (i.e., extended Yale- B, Yale, and AR) and uncontrolled (PubFig) benchmark face databases. Experimental results show its effectiveness.

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