Title :
Maximum Correntropy Criterion for Robust Face Recognition
Author :
He, Ran ; Zheng, Wei Shi ; Hu, Bao Gang
Author_Institution :
Inst. of Autom., Chinese Acad. of Sci., Beijing, China
Abstract :
In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l1norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we in particular impose nonnegativity constraint on the variables in the maximum correntropy criterion and develop a half-quadratic optimization technique to approximately maximize the objective function in an alternating way so that the complex optimization problem is reduced to learning a sparse representation through a weighted linear least squares problem with nonnegativity constraint at each iteration. Our extensive experiments demonstrate that the proposed method is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods. In particular, it shows that the proposed method can improve both recognition accuracy and receiver operator characteristic (ROC) curves, while the computational cost is much lower than the SRC algorithms.
Keywords :
face recognition; image representation; iterative methods; least squares approximations; optimisation; pattern classification; face images; half quadratic optimization technique; iteration; l1norm based sparse representation classifier; maximum correntropy criterion; nonnegativity constraint; receiver operator characteristic curves; robust face recognition; robust sparse representations; weighted linear least squares problem; Face; Face recognition; Kernel; Noise; Optimization; Robustness; Training; Information theoretical learning; M-estimator; correntropy; face recognition; half-quadratic optimization; linear least squares; occlusion and corruption.; sparse representation;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2010.220