1 March 2017 GB(2D)2 PCA-based convolutional network for face recognition
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Face recognition is a challenging task in computer vision. Numerous efforts have been made to design low-level hand-crafted features for face recognition. Low-level hand-crafted features highly depend on prior knowledge, which is difficult to obtain without learning new domain knowledge. Recently, ConvNets have generated great attention for their ability of feature learning and achieved state-of-the-art results on many computer vision tasks. However, typical ConvNets are trained by a gradient descent method in supervised mode, which results in high computational complexity. To solve this problem, an efficient unsupervised deep learning network is proposed for face recognition in this paper, which combines both 2-D Gabor filters and ( 2 D ) 2 PCA to learn the multistage convolutional filters. To speed up the calculation, the learned high-dimensional features are further encoded using short binary hashes. Finally, the obtained output features are trained using LinearSVM. Extensive experimental results on several facial benchmark databases show that the proposed network can obtain competitive performance and robust distortion-tolerance for face recognition.
© 2017 SPIE and IS&T
Min Jiang, Min Jiang, Ruru Lu, Ruru Lu, Jun Kong, Jun Kong, Xiao-Jun Wu, Xiao-Jun Wu, Hongtao Huo, Hongtao Huo, Xiaofeng Wang, Xiaofeng Wang, } "GB(2D)2 PCA-based convolutional network for face recognition," Journal of Electronic Imaging 26(2), 023001 (1 March 2017). https://doi.org/10.1117/1.JEI.26.2.023001 . Submission: Received: 11 July 2016; Accepted: 7 February 2017
Received: 11 July 2016; Accepted: 7 February 2017; Published: 1 March 2017

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