Eigen-patch: Position-patch based face hallucination using eigen transformation

Face hallucination increases the resolution of facial images and can be employed in video surveillance applications. Conventional approaches based on principal component analysis or position-patch suffers from the artifacts, low sharpness, and significant quality degradation for dis-aligned input images. In this paper, a novel face hallucination approach called eigen-patch is proposed. It combines eigen transformation with the concept of position-patch to increase local details while maintaining computation efficiency. Moreover, an image alignment procedure is proposed to align the input image to the database with multiple hypothesis verification. In addition, a re-projection procedure is also proposed to maintain the fidelity of the whole system. Experimental results show that the proposed scheme can improve the resolution of the input facial image faithfully, and it is more robust to dis-alignment between the input image and database.