Introduction to face detection using eigenfaces

被引:5
作者
Khan, Abdul Samad [1 ]
Alizai, Lawang Khan [1 ]
机构
[1] BUITMS, Quetta, Pakistan
来源
SECOND INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES 2006, PROCEEDINGS | 2006年
关键词
principal component analvsis (PCA); artiflcial neural network (ANN);
D O I
10.1109/ICET.2006.335908
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The security of information and physical property has been one of the problems of modern days. Face recognition has gained importance in security and information access. In this paper appearance based algorithm eigenfaces will be explained in detail. PCA will be used in extracting the relevant information in human faces. In this method the Eigenvectors of the set of training images are calculated which defines the face space. Face images are projected on to the face space which encodes the variation among known face images. These encoded variations are later used for recognition process. The new,face to be matched is projected on the face space, Which generates meaningful variations matching is done when variation difference between new image and training images is below some threshold value.
引用
收藏
页码:128 / +
页数:2
相关论文
共 7 条
[1]  
[Anonymous], 2002, IEEE T PATTERN ANAL
[2]  
[Anonymous], 2000, ANAL PCA BASED FACE
[3]  
BUCHALA S, PRINCIPAL COMPONENT
[4]   Suppression of high-fidelity double-strand break repair in mammalian chromosomes by pifithrin-α, a chemical inhibitor of p53 [J].
Lin, YF ;
Waldman, BC ;
Waldman, AS .
DNA REPAIR, 2003, 2 (01) :1-11
[5]  
NAVEED S, EIGENFACES PATTERN M
[6]   EIGENFACES FOR RECOGNITION [J].
TURK, M ;
PENTLAND, A .
JOURNAL OF COGNITIVE NEUROSCIENCE, 1991, 3 (01) :71-86
[7]  
TURK M, 2001, IEICE T INF SYST D, V84