Issue No. 08 - August (2011 vol. 33)
Ran He , Institute of Automation Chinese Academy of Sciences, Beijing and Dalian University of Technology, Dalian
Wei-Shi Zheng , Queen Mary University of London, London and Sun Yat-sen University, China
Bao-Gang Hu , Institute of Automation Chinese Academy of Sciences, Beijing
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 l^1norm-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.
Information theoretical learning, correntropy, linear least squares, half-quadratic optimization, sparse representation, M-estimator, face recognition, occlusion and corruption.
W. Zheng, R. He and B. Hu, "Maximum Correntropy Criterion for Robust Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1561-1576, 2010.