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C. Liu, S. C. Zhu and H. Y. Shum, “Learning Inhomogeneous Gibbs Model of Faces by Minimax Entropy,” IEEE International Conference on Computer Vision, Vancouver, July 2001, pp. 281-287.

has been cited by the following article:

• TITLE: Gaussian Mixture Models for Human Face Recognition under Illumination Variations

  AUTHORS: Sinjini Mitra

  KEYWORDS: Classification; Face Recognition; Mixture Models; Illumination

  JOURNAL NAME: Applied Mathematics, Vol.3 No.12A, December 31, 2012

  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.