Comparative evaluation of super-resolution techniques for multi-face recognition using light-field camera

Face recognition in a wide area surveillance scenario is a challenging problem as it requires multiple cameras, needs to process low resolution samples and high computation. In this paper, we experiment with a Light-Field Camera (LFC) to effectively address a wide area surveillance scenario. The interesting property of the LFC is that, it can provide different focus (or depth) images in a single capture that was not possible with any conventional camera (CCTV or PTZ) widely employed for surveillance applications. We further explore the property of multiple depth images rendered by LFC to perform the super-resolution reconstruction. To this extent, we first present a quantitative and comparative evaluation of well known super-resolution techniques. We then, present a new resolution enhancement scheme based on Discrete Wavelet Transform (DWT) as well as existing super-resolution scheme to capture the high frequency components from different depth images rendered by LFC and thereby improving the performance of the face recognition. Experiments are carried out on our new face dataset to bring out the merits and demerits of employing the light-field camera for face recognition in wide area surveillance applications.