Face recognition technique using Gabor wavelets and Singular Value Decomposition (SVD)

Abstract

Gabor wavelets (also known as Gabor filters) and Singular Value Decomposition (SVD) have been exploited extensively in the area of face recognition. In this paper, a face recognition system is developed combining features extracted using both Gabor wavelets and SVD. For Gabor wavelets, the extracted feature vectors are selected from only 12 out of 40 Gabor wavelets. The outputs from the 12 filters are selected because it provides relatively more prominent features than the other. This offers the advantage of reducing computational time. As for SVD, only the first five singular values are selected and its associated right singular vectors are used as the feature vectors. The five singular vectors are the one that carry the maximal energy of the image. The combination of Gabor wavelets and SVD offers the advantage of increasing the reliability of the face recognition system. In the face verification stage, the similarity level between facial images is determined by computing the distance between the resulting facial feature vectors obtained from Gabor wavelets and SVD, respectively. The experimental result tested using JAFFE database indicates an average correct acceptance rate of 75.2% and correct rejection rate of 100%. The results show that the combined methods provide a reliable face recognition system.