[1]WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6(6):531-538.
Copy

Analysis of the influence of SPCA parameters on the recognition of a single sample face

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 6 Page number: 531-538 Column: 学术论文—机器感知与模式识别 Public date: 2011-12-25
Title:
Analysis of the influence of SPCA parameters on the recognition of a single sample face
Author(s):
WANG Kejun;  ZOU Guofeng;  ZHANG Jie
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
face recognition; singular value decomposition;  (PC)2A;  SPCA;  derived image;  combined image
CLC:
TP391.4
DOI:
-
Abstract:
Singular value decomposition perturbation principal component analysis (SPCA) is an effective singlesample face recognition method; however, the identification results of the SPCA algorithm are seriously affected by parameter selection. In this paper, the effect on the identification, which was caused by the derived image parameter and the combined image generation parameter in the SPCA algorithm, was analyzed. Many experiments and comparative analyses were performed on the basis of the ORL face database and the CASPEAL face database. The experimental results show that the SPCA parameter selection method and the parameter range given in this paper are reasonable. In addition, reasonable parameters are effective in improving practical application of SPCA algorithms and the recognition performance of a singlesample face.
References:
［1］TAN Xiaoyang，CHEN Songcan， ZHOU Zhihua， ZHANG Fuyan. Face recognition from a single image per person: a survey［J］. Pattern Recognition, 2006, 39: 17251745.
［2］王科俊，段胜利，冯伟兴. 单训练样本人脸识别技术综述［J］.模式识别与人工智能， 2008, 21(5): 7783， 635642.
WANG Kejun, DUAN Shengli, FENG Weixing. A survey of face recognition using single training sample［J］. Pattern Recognition and Artificial Intelligence, 2008, 21(5): 7783， 635642.
［3］ZHANG Daoqiang, CHEN Songcan, ZHOU Zhihua. A new face recognition method based on SVD perturbation for single example image per person［J］. Applied Mathematics and Computation, 2005, 163(2): 895907.
［4］TURK M, PENTLAND A. Eigenfaces for recognition［J］. Journal of Cognitive Neuroscience, 1991, 3(1): 7186.
［5］KIROBY M, SIROVICH L. Application of the KarhunenLoeve procedure for the characterization of human faces［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 1990, 12(1): 103108.
［6］WU Jianxin, ZHOU Zhihua. Face recognition with one training image per person［J］. Pattern Recognition Letters, 2002, 23(14): 17111719.
［7］CHEN Songcan, ZHANG Daoxiang，ZHOU Zhihua. Enhanced (PC)2A for face recognition with one training image per person［J］. Pattern Recognition Letters， 2004， 25(10)： 11731181.
［8］何家忠，杜明辉.单样本人脸识别中一种新的图像增强方法［J］.微计算机信息， 2006, 22(3): 266268.
HE Jiazhong,DU Minghui. A new image enhancement method for face recognition with single training sample［J］.Microcomputer Information, 2006, 22(3): 266268.
［9］徐士良.C常用算法程序集［M］. 2版. 清华大学出版社， 1996: 7477.
［10］剑桥大学AT&T实验室.ORL人脸库建［EB/OL］.［20100512］.http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html.
Similar References:

Memo

-

Last Update: 2012-02-29

Copyright © CAAI Transactions on Intelligent Systems