DocumentCode :
77930
Title :
Unitary Regression Classification With Total Minimum Projection Error for Face Recognition
Author :
Shih-Ming Huang ; Jar-Ferr Yang
Author_Institution :
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume :
20
Issue :
5
fYear :
2013
fDate :
May-13
Firstpage :
443
Lastpage :
446
Abstract :
In this letter, we propose a unitary regression classification (URC) algorithm, which could achieve total minimum projection error, to improve the robustness of face recognition. Starting from linear regression classification, the goal of the proposed URC method is to minimize the total within-class projection error of all classes to seek the unitary projection for face classification. In the recognition phase, the recognition is determined by calculating the minimum projection error on the unitary rotation subspace. Experimental results carried out on FEI and FERET facial databases reveal that the proposed algorithm outperforms the state-of-the-art methods in face recognition.
Keywords :
error analysis; face recognition; image classification; regression analysis; FEI facial database; FERET facial database; URC algorithm; face classifiation; face recognition robustness improvement; linear regression classification; total minimum projection error; total within-class projection error minimization; unitary projection; unitary regression classification algorithm; unitary rotation subspace; Classification algorithms; Face recognition; Linear regression; Principal component analysis; Reactive power; Training; Vectors; Face recognition; linear regression classification; unitary regression classification;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2013.2250957
Filename :
6472780
Link To Document :
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