From Local Geometry to Global Structure: Learning Latent Subspace for Low-resolution Face Image Recognition

In this letter, we propose a novel approach for learning coupled mappings to improve the performance of low-resolution (LR) face image recognition. The coupled mappings aim to project the LR probe images and high-resolution (HR) gallery images into a unified latent subspace, which is efficient to measure the similarity of face images with different resolutions. In the training phase, we first construct local optimization for each training sample according to the relationship of neighboring data points. The local optimization aims to: (1) ensure the consistency for each LR face image and corresponding HR one; (2) model the intrinsic geometric structure between each given sample and its neighbors; and (3) preserve the discriminative information across different subjects. We finally incorporate the local optimizations together for building the global structure. The coupled mappings can be learned by solving a standard eigen-decomposition problem, which avoids the small-sample-size problem. Experimental results demonstrate the effectiveness of the proposed method on public face databases.