Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56574
Type: Report
Title: Subspace-based face recognition: outlier detection and a new distance criterion
Author: Chen, Pei
Suter, David
Publisher: Monash University
Issue Date: 2003
Series/Report no.: Technical report ; MECSE-5-2003
School/Discipline: School of Computer Science
Statement of
Responsibility: 
Pei Chen and David Suter
Abstract: Illumination effects, including shadows and varying lighting, makes the problem of face recognition challenging. Experimental and theoretical results show that the face images under different illumination conditions lie in a low-dimensional subspace, hence principal component analysis (PCA) or low-dimensional subspace techniques have been used. Following this spirit, we propose new techniques for the face recognition problem, including an outlier detection strategy (mainly for those points not following the Lambertian reflectance model), and a new Bayesian-based error criterion for the recognition algorithm. Experiments using the Yale-B face database show the effectiveness of the new strategies
Keywords: Face recognition; Linear subspace;Principal component analysis; Illuminationeffect
RMID: 0020094196
Published version: http://www.ecse.monash.edu.au/techrep/reports/
Appears in Collections:Computer Science publications

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