Face recognition based on multi-class mapping of Fisher scores

Authors:
Ling Chen;Hong Man;Ara V. Nefian
Affiliations:
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030, USA;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030, USA;Microprocessor Research Labs, Intel Corporation, SC12-303, 2200 Mission College Blvd., Santa Clara, CA 95052-8119, USA
Venue:
Pattern Recognition
Year:
2005

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Abstract

A new hidden Markov model (HMM) based feature generation scheme is proposed for face recognition (FR) in this paper. In this scheme, HMM method is used to model classes of face images. A set of Fisher scores is calculated through partial derivative analysis of the parameters estimated in each HMM. These Fisher scores are further combined with some traditional features such as log-likelihood and appearance based features to form feature vectors that exploit the strengths of both local and holistic features of human face. Linear discriminant analysis (LDA) is then applied to analyze these feature vectors for FR. Performance improvements are observed over stand-alone HMM method and Fisher face method which uses appearance based feature vectors. A further study reveals that, by reducing the number of models involved in the training and testing stages of LDA, the proposed feature generation scheme can maintain very high discriminative power at much lower computational complexity comparing to the traditional HMM based FR system. Experimental results on a public available face database are provided to demonstrate the viability of this scheme.