Combination of fisher scores and appearance based features for face recognition

Ling Chen, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

A novel feature generation scheme which combines multi-class mapping of Fisher scores and appearance based features for face recognition (FR) is proposed in this paper. Multi-class mapping of Fisher scores is based on partial derivative analysis of parameters of hidden Markov model (HMM), and appearance based features are obtained directed from face images. Linear discriminant analysis (LDA) is used to analyze the feature vectors generated under this scheme. Recognition performance improvement is observed over stand-alone HMM method as well as Fisherface method, which also uses appearance based feature vectors. Moreover, by reducing the number of models involved in the training and testing stages, the proposed feature generation scheme can maintain very high discriminative power at much lower computational complexity comparing to that of the traditional HMM based FR system. Experimental results are provided to demonstrate the viability of this scheme for face recognition.

Original languageEnglish
Title of host publicationProceedings of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications, WBMA 2003
Pages74-81
Number of pages8
ISBN (Electronic)1581137796, 9781581137798
DOIs
StatePublished - 8 Nov 2003
Event2003 ACM SIGMM Workshop on Biometrics Methods and Applications, WBMA 2003 - Berkley, United States
Duration: 8 Nov 2003 → …

Publication series

NameProceedings of the 2003 ACM SIGMM Workshop on Biometrics Methods and Applications, WBMA 2003

Conference

Conference2003 ACM SIGMM Workshop on Biometrics Methods and Applications, WBMA 2003
Country/TerritoryUnited States
CityBerkley
Period8/11/03 → …

Keywords

  • Fisher score
  • Hidden Markov model
  • Linear discriminant analysis

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