收稿日期: 2016-07-03
网络出版日期: 2018-06-27
基金资助
国家重大科学仪器设备开发专项基金项目(2012YQ150087);上海市科委重点基金资助项目(14DZ1206302)
Improved softmax classifier for deep convolution neural networks and its application in face recognition
Received date: 2016-07-03
Online published: 2018-06-27
提出了一种有效的特征学习方法, 构建了9层结构的卷积神经网络,利用Softmax回归算法进行人脸分类识别.卷积神经网络在输出层利用改进的Softmax进行分类,在隐藏层采用修正线性单元作为激活函数,并在网络中加入局部响应归一化处理, 有效避免了梯度消失问题.利用大量人脸图像数据对网络进行预训练, 得到较好的网络初始权重.在针对YALE, FERET, LFW-A等人脸数据库进行人脸识别实验中,与现有的几种人脸识别方法SDAEs, RRC, MPCRC, CRC,SRC等进行对比表明, 该方法在各人脸数据库的识别中均得到较高的识别率.
冉鹏, 王灵, 李昕, 刘鹏伟 . 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[J]. 上海大学学报(自然科学版), 2018 , 24(3) : 352 -366 . DOI: 10.12066/j.issn.1007-2861.1831
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.
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