Robust group sparse representation via half-quadratic optimization for face recognition

— Sparse representation-based classifier (SRC), which represents a test sample with a linear combination of training samples, has shown promise in pattern classification. However, there are two shortcomings in SRC: (1) the ℓ2norm used to measure the reconstruction fidelity is noisesensitive and (2) the ℓ1-norm induced sparsity did not consider the correlation among the training samples. Furthermore, in real applications, face images with similar variations, such as illumination or expression, often have higher correlation than those from the same subject. Therefore, we propose to improve the performance of SRC from two aspects: (1) replace the noise-sensitive ℓ2-norm with an M-estimator to enhance its robustness and (2) emphasize the sparsity of the number of classes instead of the number of training samples, which leads to the group sparsity. The proposed robust group sparse representation (RGSR) can be efficiently optimized via alternating minimization under the Half-Quad...

• Type: Journal
• Year: 2015
• Where: CCECE
• Authors: Yong Peng, Bao-Liang Lu
• Keywords: CCECE 2015, Electrical And Computer Engineering