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GB(2D)2 PCA-based convolutional network for face recognition

[+] Author Affiliations
Min Jiang, Ruru Lu, Xiao-Jun Wu, Xiaofeng Wang

Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Wuxi, China

Jun Kong

Jiangnan University, Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Wuxi, China

Xinjiang University, College of Electrical Engineering, Urumqi, China

Hongtao Huo

People’s Public Security University of China, Department of Information Security Engineering, Beijing, China

J. Electron. Imaging. 26(2), 023001 (Mar 01, 2017). doi:10.1117/1.JEI.26.2.023001
History: Received July 11, 2016; Accepted February 7, 2017
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Abstract.  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 (2D)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.

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Citation

Min Jiang ; Ruru Lu ; Jun Kong ; Xiao-Jun Wu ; Hongtao Huo, et al.
"GB(2D)2 PCA-based convolutional network for face recognition", J. Electron. Imaging. 26(2), 023001 (Mar 01, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023001


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