A novel image fusion scheme for robust multiple face recognition with light-field camera

Accurate face recognition in wide area surveillance application is a challenging problem because of multiple pose, non-uniform illumination, low resolution and out-of-focus face images that are recorded with conventional surveillance cameras (Closed-Circuit TeleVision or Pan-Tilt-Zoom). In this paper, we address the problem of face recognition in wide area surveillance with a light-field camera. The main advantage of a light-field camera is that, it can provide different focus (or depth) images in a single exposure (capture) which is not possible with a conventional 2D camera. In this work, we propose a novel weighted image fusion scheme to combine different depth (or focus) images rendered by a light-field camera. The proposed image fusion scheme is not only dynamic in handling number of depth (or focus) images but also adaptive in assigning higher weights to the best focus image as compared to the out-of-focus image. Extensive experiments are carried out on our newly acquired face dataset captured using Lytro light-field camera to bring out the merits and demerits of the proposed weighted image fusion scheme for face recognition in wide area surveillance applications.