Abstract： For the method of conventional low rank representation (LRR), the representation coefficients of test samples are calculated based on the learned dictionary, which results in high computational complexity and low correlation of representation coefficients between training and test samples. To solve the problems, a structured localityconstrained low rank representation (SLCLRR) method was proposed for face recognition. An ideal regularization term was introduced in LRR to encourage the representation matrix of training data with blockdiagonal structure. A locality constraint was incorporated to acknowledge the intrinsic manifold structure of training data and make the similar samples with similar representations. Test samples were classified by a simple yet effective linear classifier. The verified experiments were conducted on the four benchmark datasets of AR, Extended Yale B, ORL and LFW. The results show that the proposed method can obtain the representation coefficients of training and test samples simultaneously with good robustness for occlusions, pixel corruptions and illumination variations in the face images.
