Face hallucination with imprecise-alignment using iterative sparse representation

• Propose a face hallucination method with mis-aligned face images.
• Global model based on discriminative reconstruction properties of sparse coding.
• Local model based on coupled visual vocabulary.
• Optimize the combination of global and local steps in an iterative manner.
• Experimental results show a fast convergence and good performance.

Existing face hallucination methods assume that the face images are well-aligned. However, in practice, given a low-resolution face image, it is very difficult to perform precise alignment. As a result, the quality of the super-resolved image is degraded dramatically. In this paper, we propose a near frontal-view face hallucination method which is robust to face image mis-alignment. Based on the discriminative nature of sparse representation, we propose a global face sparse representation model that can reconstruct images with mis-alignment variations. We further propose an iterative method combining the global sparse representation and the local linear regression using the Expectation Maximization (EM) algorithm, in which the face hallucination is converted into a parameter estimation problem with incomplete data. Since the proposed algorithm is independent of the face similarity resulting from precise alignment, the proposed algorithm is robust to mis-alignment. In addition, the proposed iterative manner not only combines the merits of the global and local face hallucination, but also provides a convenient way to integrate different strategies to handle the mis-alignment problem. Experimental results show that the proposed method achieves better performance than existing methods, especially for mis-aligned face images.