Robust Kernel Representation with Statistical Local Features for Face Recognition

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Authors Meng Yang, Lei Zhang, Simon C. K. Shiu, and David Zhang
Journal/Conference Name IEEE Transactions on Neural Networks and Learning Systems
Paper Category
Paper Abstract Factors such as misalignment, pose variation and occlusion make robust face recognition a difficult problem. It is known that statistical features such as LBP are effective for local feature extraction, while the recently proposed sparse or collaborative representation based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. First, multi-partition max pooling is used to enhance the SLF’s invariance to image registration error. Then, a kernel based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including Extended Yale B, AR, Multi-PIE, FERET, FRGC and LFW, which have various variations of lighting, expression, pose and occlusions, demonstrating the promising performance of the proposed method.
Date of publication 2013
Code Programming Language MATLAB
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