Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition

NEUROCOMPUTING

卷:133

Article

Kang, Cuicui¹  Liao, Shengcai¹  Xiang, Shiming¹  Pan, Chunhong¹ 

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China

关键词: l(1)-minimization;    Sparse representation classification;    Kernels;    Coordinate descent;    Face recognition;    LBP;   

DOI  :  10.1016/j.neucom.2013.11.022

来源: Elsevier

【 摘 要 】

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 KCDSRC 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. (C) 2014 Elsevier B.V. All rights reserved.

【 授权许可】

Free