Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition

Abstract

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms. In his paper we report performance analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. This analysis was carried out on various current PCA and LDA based face recognition algorithms using standard public databases. Among various PCA algorithms analyzed, Manual face localization used on ORL and SHEFFIELD database consisting of 100 components gives the best face recognition rate of 100%, the next best was 99.70% face recognition rate using PCA based Immune Networks (PCA-IN) on ORL database. Among various LDA algorithms analyzed, Illumination Adaptive Linear Discriminant Analysis (IALDA) gives the best face recognition rate of 98.9% on CMU PIE database, the next best was 98.125% using Fuzzy Fisherface through genetic algorithm on ORL database. In this paper we report performance analysis of various current PCA and LDA based algorithms for face recognition. The evaluation parameter for the study is face recognition rate on various standard public databases. The remaining of the paper is organized as follows: Section II provides a brief overview of PCA, Section III presents PCA algorithms analysed, Section IV provides brief overview of LDA, Section V presents LDA algorithms analysed. Section VI presents performance analysis of various PCA and LDA based algorithms finally Section VII draws the conclusion. II. PRINCIPAL COMPONENT ANALYSIS (PCA)