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
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2250957