Super-Resolution for High Magnification Face Images

摘要

Most existing face recognition algorithms require face images with a minimum resolution. Meanwhile, the rapidly emerging need for near-ground long range surveillance calls for a migration in face recognition from close-up distances to long distances and accordingly from low and constant resolution to high and adjustable resolution. With limited optical zoom capability restricted by the system hardware configuration, super-resolution (SR) provides a promising solution with no additional hardware requirements. In this paper, a brief review of existing SR algorithms is conducted and their capability of improving face recognition rates (FRR) for long range face images is studied. Algorithms applicable to real-time scenarios are implemented and their performances in terms of FRR are examined using the IRIS-LRHM face database [1]. Our experimental results show that SR followed by appropriate enhancement, such as wavelet based processing, is able to achieve comparable FRR when equivalent optical zoom is employed.