“Comparative Analysis of Face Recognition Methods”

Abstract

Biometric systems have been researched intensively for security issues. Biometric systems can uniquely identify a particular identity. Among the biometric systems face recognition system is one of the most popular. In this approach the individuals are identified by the feature of face. Research has been in progress since 1980’s with numerous applications henceforth. Currently, many face recognition applications are available commercially for criminal identification, security system, image processing etc. Face recognition is a popular research area where there are different approaches studied in the literature. The goal of face recognition system is straightforward; Compare the captured images with images stored in database and recognize the faces already stored in database. In this thesis, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method, Linear Discriminator Analysis (LDA) based method, namely Fisherfaces and Independent Component Analysis (ICA) based method are studied in detail. These algorithms are studied in detail and these three methods are compared.