Face recognition based on multi-class mapping of Fisher scores

Ling Chen, Hong Man, Ara V. Nefian

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

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.

Original languageEnglish
Pages (from-to)799-811
Number of pages13
JournalPattern Recognition
Volume38
Issue number6
DOIs
StatePublished - Jun 2005

Keywords

  • Face recognition
  • Fisher score
  • Hidden Markov model
  • Linear discriminant analysis

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