Position-patch based face hallucination via locality-constrained representation

Instead of using probabilistic graph based or manifold learning based models, some approaches based on position-patch have been proposed for face hallucination recently. In order to obtain the optimal weights for face hallucination, they represent image patches through those patches at the same position of training face images by employing least square estimation or convex optimization. However, they can hope neither to provide unbiased solutions nor to satisfy locality conditions, thus the obtained patch representation is not the best. In this paper, a simpler but more effective representation scheme- Locality-constrained Representation (LcR) has been developed, compared with the Least Square Representation (LSR) and Sparse Representation (SR). It imposes a locality constraint onto the least square inversion problem to reach sparsity and locality simultaneously. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art face hallucination approaches.