Face recognition based on BP nural networks and principle component analysis

Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (36): 226-229.

• 工程与应用 • Previous Articles Next Articles

Face recognition based on BP nural networks and principle component analysis

ZHAO Li-qiang¹,ZHANG Xiao-hua^1,2,GAO Zhen-bo³,ZHANG Hong-liang¹

1．Department of Mathematics and Physics，Hebei Normal University of Science and Technology，Qinhuangdao，Hebei 066004，China
2．College of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，China
3．Department of Computer，Hebei Normal University of Science and Technology，Qinhuangdao，Hebei 066004，China

Received:1900-01-01 Revised:1900-01-01 Online:2007-12-21 Published:2007-12-21
Contact: ZHAO Li-qiang

基于BP神经网络的主分量分析人脸识别算法

赵立强¹,张晓华^1,2,高振波³,张洪亮¹

1．河北科技师范学院数理系，河北秦皇岛 066004
2．哈尔滨理工大学计算机科学与技术学院，哈尔滨 150080
3．河北科技师范学院计算机系，河北秦皇岛 066004

通讯作者: 赵立强

Abstract

Abstract: BP neural network combined with principal component analysis is applied to human face recognition.After extracting low frequency sub-band of face image in wavelet transform，the eigenface space is constructed by PCA.Then all samples are projected into the subspace，the coefficient of every sample is inputted to BP neural network，and the face recognizer consists of BP neural network and post-probability converter.The experiments on ORL face database indicate the recognition ratio is greatly improved.

Key words: face recognition, BP neural network, principle component analysis（PCA）, wavelet transform

摘要： 提出了基于BP神经网络的主分量人脸识别算法。该算法首先用小波变换对人脸图像进行小波分解，形成低频小波子图，然后用主分量分析法构造特征脸子空间，将人脸图像在特征空间的投影作为BP神经网络的输入，由BP神经网络和后验概率转换器构成人脸识别器。针对ORL人脸库的实验结果表明该方法具有较高的识别率。

关键词: 人脸识别, BP神经网络, 主分量分析（PCA）, 小波变换

ZHAO Li-qiang¹,ZHANG Xiao-hua^1,2,GAO Zhen-bo³,ZHANG Hong-liang¹. Face recognition based on BP nural networks and principle component analysis[J]. Computer Engineering and Applications, 2007, 43(36): 226-229.

赵立强¹,张晓华^1,2,高振波³,张洪亮¹. 基于BP神经网络的主分量分析人脸识别算法[J]. 计算机工程与应用, 2007, 43(36): 226-229.

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Face recognition based on BP nural networks and principle component analysis

基于BP神经网络的主分量分析人脸识别算法

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