Robust framework of single-frame face superresolution across head pose, facial expression, and illumination variations

This paper presents a robust framework to solve the face hallucination problem across multiple factors, i.e., different expressions, head poses, and illuminations. It proposes a redundant transformation with diagonal loading for modeling the mappings among different new face factors, and a local reconstruction with geometry and position constraints for incorporating image details in the new factor spaces. Our proposed redundant and sparse strategies are discussed, and the experiments indicate that it is not necessary to adopt sparse representation in the proposed framework. The experimental results demonstrate that the proposed framework offers robustness when dealing with the inputs that have different expressions, head poses, and illuminations compared with the state-of-the-art methods, can generate high-resolution face images with better image qualities than the hierarchical tensor-based method, and improves the state of the art from single one output to multiple outputs with new factors.