Multi-view face hallucination based on sparse representation

In this paper, we propose a novel method to generate the hallucinated multi-views of faces using the sparse-representation model. In order to render a faithful virtual view, we introduce centralized constraints into a variation framework for optimization. The constraints are formulated based on an attempt to minimize the difference between the sparse-coding coefficients derived for two distinct views. In our algorithm, sift optical-flow method is employed to formulate the constraints. An input face is firstly sparsely coded over a given dictionary, and then the sparse-coding coefficients for the input face are refined through an optimization framework with the centralized constraints. Intensive experimental results demonstrate that our proposed method can perform well in terms of both reconstruction accuracy and visual quality.