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Learning Generalized Deep Feature Representation for Face Anti-Spoofing | IEEE Journals & Magazine | IEEE Xplore

Learning Generalized Deep Feature Representation for Face Anti-Spoofing


Abstract:

In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detecti...Show More

Abstract:

In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking both spatial and temporal information into consideration, and a 3D convolutional neural network architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods.
Published in: IEEE Transactions on Information Forensics and Security ( Volume: 13, Issue: 10, October 2018)
Page(s): 2639 - 2652
Date of Publication: 11 April 2018

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