Experimental Evaluation of Feature Extraction Schemes for Face Recognition

ABSTRACT: In this thesis, we studied the use of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Gabor wavelets for face recognition. Both PCA and LDA are applied for the extraction of features from the raw pixel values. Then, their use for the extraction of features from the outputs of Gabor wavelets is considered. Lattice-based selection of a subset of Gabor outputs is considered for this purpose. A rectangular grid of various sizes is considered and the Gabor filter outputs extracted from the grid points are employed for feature extraction using PCA and LDA. As an alternative approach, Best Individual Selection (BIS) and Sequential Forward Selection (SFS) are employed for feature subset selection. The k nearest neighbor classifier is employed as the classification scheme. The experiments have been carried out on FERET database. It is observed that the accuracies achieved using Gabor wavelets are superior when compared to the features derived from the raw pixel values. Moreover, superior scores are generally achieved using BIS and SFS approaches when compared to PCA and LDA. Keywords: Face recognition, sequential feature selection, best individual selection, Gabor wavelets, principal component analysis, linear discriminant analysis . …………………………………………………………………………………………………………………………