Label propagation algorithm based on non-negative sparse representation
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Joint dynamic sparse representation for multi-view face recognition
Pattern Recognition
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Letters: Enhancing sparsity via ℓp (0
Neurocomputing
Correntropy-Based document clustering via nonnegative matrix factorization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Weighted group sparse representation based on robust regression for face recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Neurocomputing
Pattern Recognition Letters
Robust spectral regression for face recognition
Neurocomputing
Robust face recognition via occlusion dictionary learning
Pattern Recognition
Hi-index | 0.14 |
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.