IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
A Hierarchical Face Recognition Method Based on Local Binary Pattern
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel sparse representation for image classification and face recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
IEEE Transactions on Neural Networks
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Face recognition has been popular in the pattern recognition field for decades, but it is still a difficult problem due to the various image distortions. Recently, sparse representation based classification (SRC) was proposed as a novel image classification approach, which is very effective with sufficient training samples for each class. However, the performance drops when the number of training samples is limited. In this paper, we show that effective local image features and appropriate nonlinear kernels are needed in deriving a better classification method based on sparse representation. Thus, we propose a novel kernel SRC framework and utilize effective local image features in this framework for robust face recognition. First, we present a kernel coordinate descent (KCD) algorithm for the LASSO problem in the kernel space, and we successfully integrate it in the SRC framework (called KCD-SRC) for face recognition. Second, we employ local image features and develop both pixel-level and region-level kernels for KCD-SRC based face recognition, making it discriminative and robust against illumination variations and occlusions. Extensive experiments are conducted on three public face databases (Extended YaleB, CMU-PIE and AR) under illumination variations, noise corruptions, continuous occlusions, and registration errors, demonstrating excellent performances of the KCD-SRC algorithm combining with the proposed kernels.