Gaussian Mixture Models for Human Face Recognition under Illumination Variations

Sinjini Mitra

Information Systems and Decision Sciences Department, Mihaylo College of Business and Economics, California State University, Fullerton, USA.

DOI: 10.4236/am.2012.312A286                

Abstract

The appearance of a face is severely altered by illumination conditions that makes automatic face recognition a challenging task. In this paper we propose a Gaussian Mixture Models (GMM)-based human face identification technique built in the Fourier or frequency domain that is robust to illumination changes and does not require “illumination normalization” (removal of illumination effects) prior to application unlike many existing methods. The importance of the Fourier domain phase in human face identification is a well-established fact in signal processing. A maximum a posteriori (or, MAP) estimate based on the posterior likelihood is used to perform identification, achieving misclassification error rates as low as 2% on a database that contains images of 65 individuals under 21 different illumination conditions. Furthermore, a misclassification rate of 3.5% is observed on the Yale database with 10 people and 64 different illumination conditions. Both these sets of results are significantly better than those obtained from traditional PCA and LDA classifiers. Statistical analysis pertaining to model selection is also presented.

Keywords

Classification; Face Recognition; Mixture Models; Illumination

Conflicts of Interest

The authors declare no conflicts of interest.

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