SVD-Based Projection for Face Recognition Chou-Hao Hsu and Chaur-Chin Chen Department of Computer Science Institute of Information Systems & Applications National Tsing Hua University, Hsinchu,Taiwan

Training Face Images Let F i (j) be the ith face image of m by n from the jth subject, 1 ≦ i ≦ N j and 1 ≦ j ≦ K, N 1 +N 2 +….+N k =N be the training face images. Define the mean image S as

Singular Value Decomposition S=UDV t Do S=UDV t where U and V are orthogonal. Select r,c with r ≦ m, c ≦ n such that d 11 +d 22 +…+d hh ≧ 85% of trace(D), where h=min{r,c} Let U r =[u 1,u 2,...,u r ], V c =[v 1,v 2,…,v c ] Where U is an m by m orthogonal matrix V is an n by n orthogonal matrix

Convert a face image into features For each training image A k, we represent this A k as x k =(U r ) t AV c, an r by c feature image For each test image T, we represent T by y=(U r ) t TV c, an r by c feature image

Distance between training feature images and a test feature image Compute d(y,x k ) by Fröbenius norm The smaller Fröbenius norm, the closer Rank the norms in an ascending order Determine the recognition rates from ranks 1, 2, 3,...,8 and plot the curve

Part of 5*40 Training Face Images

Missed Face Images and Their Wrongly-Best Matched Images

A Comparison of Difference Projection Methods