Fusion of Qualities for Frame Selection in Video Face Verification

It is known that the use of video can help improve the performance of face verification systems. However, processing video in resource constrained devices is prohibitive. In order to reduce the load of the algorithms, a quality-based selection of frames can be applied. Generally there are available several qualities and thus a good fusion scheme is required. This paper addresses the problem of fusing quality measures such that the resulting quality improves the performance of frame selection. A comparison of different methods for fusing qualities is presented. Also, some new quality measures based on time derivatives are proposed, which are shown to be beneficial for estimating the overall quality. Finally, a curve is proposed which proves that the qualities used for frame selection effectively improve verification performance, independent of the number of frames selected or the method employed for obtaining the overall biometric score.