Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition - Kang, Cuicui; Liao, Shengcai; Xiang, Shiming; Pan, Chunhong (Neurocomputing (Amsterdam), 2014)
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. •Suggests the need of local feature based kernels for SRC under small sample size.•Proposes KCD, a kernel based ℓ1-norm minimization method for face recognition.•Proposes LBPh−KH an effective local image feature based kernel.•LBPh−KH+KCD−SRC outperforms SRC by 20%+ under 5 training images per subject.