哈尔滨商业大学学报（自然科学版）2017，Vol.33Issue(5)：570-574,5.
卷积神经网络在人脸识别上的研究
Research of convolution neural network on face recognition
聂超1
作者信息
- 1. 中国海洋大学数学科学学院,青岛266100
- 折叠
摘要
Abstract
Convolution Neural Network (CNN) has achieved promising results in face recognition recently .The verification recognition accuracy of some very deep neural network has reached 99%, exceeding person's performance.However, these networks need to train a vast number of parameters or huge dataset.In this paper, a smaller network, greatly decreasing train parameters and obtaining better recognition rates within limited dataset was put forward .In order to deeper understand convolution neural network , we made many experiments had been made .The effects of normalization before and after the active layer on network training were compared .Meanwhile, the effects of different feature dimensionality on recognition accuracy were compared , and the effects of Euclid distance and cosine distance on recognition accuracy were compared .From the results of experiments , the network which normalization was before the active layer gets higher accuracy .The way of using cosine distance was better than that using Euclid distance , and the feature dimensionality and accuracy were higher .关键词
深度学习/人脸识别/卷积神经网络Key words
deep learning/face recognition/convolution neural network分类
信息技术与安全科学引用本文复制引用
聂超..卷积神经网络在人脸识别上的研究[J].哈尔滨商业大学学报（自然科学版）,2017,33(5):570-574,5.
