On heterogeneous face recognition

Abstract

Face recognition under controlled and constrained scenarios have reached a significant level of maturity with respect to performance and reliability. However, under unconstrained and un controlled settings, current state-of-the-art face recognition systems fail to yield a consistent level of performance. In recent years, several countries have experienced a high number of terrorist attacks, events of public unrest and cross border intrusions. As a preventive and inves tigative measure, governments around the world have installed surveillance cameras in public places such as railway and bus stations, airports, shopping malls, and so on. Images acquired from these cameras (probes) are captured in an unconstrained and non-cooperative environment, hence their quality in terms of resolution, illumination, pose, spectrum and so on may vary heav ily. Images captured by these cameras are matched with a background database which contain images collected from government records such as passport, driving licenses and so on. Such images (gallery) have much better and consistent quality. The matching of poor quality probes with good quality gallery images is a challenging problem, which involves utilizing auxiliary information (such as depth maps), improving the quality of the captured images, learning of heterogeneity aware models and matching to optimize the top-k identification accuracy. This dissertation attempts to develop effective algorithms for face recognition in unconstrained and non-cooperative scenarios where images captured are either in low resolution and/or in NIR (Near-Infrared) with low quality and inherent noise due to the in-the-wild image capture setup commonly encountered in surveillance settings. The first contribution is primarily aimed at utilizing auxiliary sources of information for training a shared representation for face recognition in unconstrained environments. Low cost depth sensors have opened new avenues for their usage in video surveillance scenarios.