Face recognition using shape and texture

Chengjun Liu, Harry Wechsler

Research output: Contribution to journalConference articlepeer-review

12 Scopus citations

Abstract

We introduce in this paper a new face coding and recognition method which employs the Enhanced FLD (Fisher Linear Discriminant) Model (EFM) on integrated shape (vector) and texture (`shape-free' image) information. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image by warping the original face image to the mean shape, i.e., the average of aligned shapes. The dimensionalities of the shape and the texture spaces are first reduced using Principal Component Analysis (PCA). The corresponding but reduced shape and texture features are then integrated through a normalization procedure to form augmented features. The dimensionality reduction procedure, constrained by EFM for enhanced generalization, maintains a proper balance between the spectral energy needs of PCA for adequate representation, and the FLD discrimination requirements, that the eigenvalues of the within-class scatter matrix should not include small trailing values after the dimensionality reduction procedure as they appear in the denominator.

Original languageEnglish (US)
Pages (from-to)598-603
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: Jun 23 1999Jun 25 1999

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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