Preprocessing using SVD towards illumination invariant face recognition

Abstract

Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing.