Facial Image Recognition Based on a Statistical Uncorrelated Near Class Discriminant Approach

Sheng LI  Xiao-Yuan JING  Lu-Sha BIAN  Shi-Qiang GAO  Qian LIU  Yong-Fang YAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.4   pp.934-937
Publication Date: 2010/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.934
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
Keyword: 
near classes,  locally statistical uncorrelated constraints,  statistical uncorrelated near class discriminant (SUNCD),  face recognition,  

Full Text: PDF(144.7KB)
>>Buy this Article


Summary: 
In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.