Learning-based image representation and method for face recognition

Document Type

Conference Proceeding

Publication Date

12-16-2009

Abstract

This paper presents a novel method for face recognition. First, we generate the new image representation from the decorrelated hybrid color configurations rather than RGB color space via a learning algorithm. The learning algorithm, Principal Component Analysis (PCA) plus Fisher Linear Discriminant analysis (FLD), is able to derive the desired color transformation to generate a discriminating image representation that is optimal for face recognition. Second, we partition face image into some small patches, each of which can obtain its own color transformation, to reduce the effect of illumination variations. Thus, a patch-based novel image representation method is proposed for face recognition. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that the proposed method outperforms gray-scale image and some recent methods in face recognition. ©2009 IEEE.

Identifier

71749117522 (Scopus)

ISBN

[9781424450206]

Publication Title

IEEE 3rd International Conference on Biometrics Theory Applications and Systems Btas 2009

External Full Text Location

https://doi.org/10.1109/BTAS.2009.5339012

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