Surveillance Face Super-Resolution via Shape Clustering and Subspace Learning

In a learning-based super-resolution algorithm, suitable prior from the training database is a key issue. A novel face hallucination algorithm based on shape clustering and subspace learning for adaptive prior is proposed in this paper. We define face shape metrics with point distribution model by Hausdorff Distance, then a framework of adaptive prior and subspace learning is proposed to enhance the performance of surveillance face super-resolution. Linear regression is used to learn the relationship between low and high image systhesis coefficients. Experiments show that the face super-resolution algorithm based on shape classification can improve the subjective and objective quality of the input low-resolution face images and outperform many state-of-the-art global-based face super-resolution methods.