Face Recognition Using Gabor Wavelet Features with PCA and KPCA - A Comparative Study

Face recognition (FR) is one of the most prominentforms of biometric recognitionthat proffersa myriad ofcross-domain applications andaugmenting its proficiency has been on the forefront of research efforts for the past two decades. The efficacy of FR systems is dictated by the choice of the feature extractor, and to that end,GABORWavelet Transform has emerged as one of the most successful methodologies for feature extraction offaces in digital images. Manyvariants have been proposed to improve performance of conventional GABOR, and therefore in this paper, we conduct a comprehensive study to provide anin-depth comparison of the efficacy of the GABOR-PCA (linear) and GABOR-KPCA (non-linear) techniques. Our experimentations have been conducted on the publicly available ORL database. We will demonstrate using pertinent mathematical arguments and extensive experimentations, the difference in performance between linear and non-linear choices, and show that the GABOR-PCA variant is better suited for FR tasks when GABOR is employed as the feature extractor. The results of this study, coupled with our other works, form a series thatis intended to assist developers in making prudent choices indesigning proficient FR systems.