Title of article :
A shape- and texture-based enhanced Fisher classifier for face recognition
Author/Authors :
Chengjun Liu، نويسنده , , Wechsler، نويسنده , , H. ، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2001
Pages :
11
From page :
598
To page :
608
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 L1 or L2 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
Keywords :
Enhanced Fisher classifier (EFC) , enhanced FLDmodel (EFM) , Face recognition , Fisher linear discriminant (FLD) , Principal component analysis (PCA) , shape and texture.
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING
Serial Year :
2001
Journal title :
IEEE TRANSACTIONS ON IMAGE PROCESSING
Record number :
396588
Link To Document :
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