Face recognition from super-resolved images

Surveillance imagery is usually lower in resolution than the capabilities of hardware owing to demands on storage and processing. Additionally, a large field of view reduces the number of pixels covered by a face even further. As this number decreases, it has been observed that face recognition performance eventually degrades considerably. Super-resolution helps overcome this by fusing complimentary information from multiples frames of video to produce higher resolution images. As many existing techniques assume rigidity of objects and simple global motion between frames, their performance suffers when applied to human faces. Optical flow can be used solve this problem by generating a dense motion field to track the inter-frame motion. This paper presents a novel optical flow based super-resolution face recognition system. Results from preliminary experiments show consistent improvement in recognition performance using super-resolved images, making the system viable for use in a surveillance environment.