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Journal of Shanghai University >

2018 , Vol. 24 >Issue 3: 352 - 366

DOI: https://doi.org/10.12066/j.issn.1007-2861.1831

Research Paper

Improved softmax classifier for deep convolution neural networks and its application in face recognition

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  • School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received date: 2016-07-03

  Online published: 2018-06-27

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Abstract

An effective learning method by constructing a 9-layer convolution neural network structure, using the softmax regression for human face classification and identification is proposed. Taking advantage of the improved softmax classifier in the output layer, using a rectified linear unit as the activation function in the hidden layer and local response normalization processes in the network, the problem of vanishing gradient is well solved. Pre-trained by using a large number of face images, proper network initial weights are obtained. Experiments based on three face databases YALE, FERET and LFW-A, demonstrate that the proposed method approaches the highest recognition rate compared with other methods such as SDAEs, RRC, MPCRC, CRC and SRC.

Key words： convolution neural network; rectified linear unit; local response normalization; face recognition

Cite this article

RAN Peng, WANG Ling, LI Xin, LIU Pengwei . Improved softmax classifier for deep convolution neural networks and its application in face recognition[J]. Journal of Shanghai University, 2018 , 24(3) : 352 -366 . DOI: 10.12066/j.issn.1007-2861.1831

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