Robust 3D Patch-Based Face Hallucination

Incorporating 3D information has proven to be effective in many computer vision tasks and it is no exception in the context of facial analysis. However, limited application has been witnessed in face hallucination (FH), probably due to the difficulty of fitting 3D models onto low-resolution (LR) images. This paper presents a pure 3D approach to address this problem. By extending the LR image formation process to the 3D domain, the classic Lucas-Kanade algorithm is exploited to improve the precision of the error-prone 3D model fitting on LR images. The established correspondence between the input image and 3D training textures then facilitates reconstruction of high-resolution (HR) patches directly on the mesh, which can be employed to render realistic frontal faces for recognition. Extensive evaluation on several publicly available datasets reveals superior qualitative and quantitative results over state-of-the-art methods in fitting, FH and recognition, which shows the advantage of the proposed 3D framework over its 2D rivals, especially for non-frontal head poses and low image quality.