Maximizing face recognition performance for video data under time constraints by using a cascade

This paper presents a cascade of video face recognition methods which maximizes the recognition performance within a given time limit. Traditionally, choosing the appropriate recognition method for a specific person identification scenario is a difficult task. The maximization of the recognition performance under restricted time is seen as the main problem for increasing sizes of video data. To address this, we first evaluate a set of common face recognition methods and identify the ones which show the best recognition performance per time. Then, they are combined in the proposed cascade. An optimization strategy is presented, that maximizes the recognition performance of the cascade within a given time limit. Especially, this is no fixed limit, instead it can be assigned for each recognition task individually. A cross dataset evaluation on the Honda/UCSD and the Face in Action datasets shows the benefits of the proposed cascade. It allows situation specific configuration and the recognition performance per time is improved in comparison to the underlying face recognition methods