Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9041
Title: Robust kernel representation with statistical local features for face recognition
Authors: Yang, M
Zhang, L 
Shiu, SCK 
Zhang, D 
Keywords: Collaborative representation
Face recognition
Robust kernel representation
Statistical local feature
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2013, v. 24, no. 6, 6471239, p. 900-912 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas 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. Initially, multipartition max pooling is used to enhance the invariance of SLF 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 (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.
URI: http://hdl.handle.net/10397/9041
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2013.2245340
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