A shape- and texture-based enhanced Fisher classifier for face recognition

Chengjun Liu, Harry Wechsler

Research output: Contribution to journalArticlepeer-review

220 Scopus citations

Abstract

This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and texture features. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image. The dimensionalities of the shape and the texture spaces are first reduced using principal component analysis, constrained by the EFM for enhanced generalization. The corresponding reduced shape and texture features are then combined through a normalization procedure to form the integrated features that are processed by the EFM for face recognition. Experimental results, using 600 face images corresponding to 200 subjects of varying illumination and facial expressions, show that 1) the integrated shape and texture features carry the most discriminating information followed in order by textures, masked images, and shape images and 2) the new coding and face recognition method, EFC, performs the best among the Eigenfaces method using L 1 or L 2 distance measure, and the Mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within-class covariance matrix. In particular, EFC achieves 98.5% recognition accuracy using only 25 features.

Original languageEnglish (US)
Pages (from-to)598-605
Number of pages8
JournalIEEE Transactions on Image Processing
Volume10
Issue number4
DOIs
StatePublished - Apr 2001
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Enhanced FLD model (EFM)
  • Enhanced Fisher classifier (EFC)
  • Face recognition
  • Fisher linear discriminant (FLD)
  • Principal component analysis (PCA)
  • Shape and texture

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