改进Softmax分类器的深度卷积神经网络及其在人脸识别中的应用

doi:10.12066/j.issn.1007-2861.1831

Abstract

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

CLC Number:

TP183

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（Natural Science Edition）, 2018, 24(3): 352-366.

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

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