Face Tracking via Content Aware Correlation Filter

Abstract

Face tracking is an importance task in many computer vision based augment reality systems. Correlation filters (CFs) have been applied with great success to several computer vision problems including object detection, classification and tracking, but few CF-based methods are proposed for face tracking. As an essential research direction in computer vision, face tracking is very important in many human-computer applications. In this paper, we present a content aware CF for face tracking. In our work, face content refers to the locality sensitive histogram based foreground feature and the learning samples extracted from complex background. It means that both foreground and background information are considered in constructing the face tracker. The foreground feature is introduced into the objective function which could learn an efficient model to adapt to the face appearance variation. For evaluating the proposed face tracker, we build a dataset which contains 97 video sequences covering the 11 challenging attributes of face tracking. Extensive experiments are conducted on the dataset and the results demonstrate that the proposed face tracker shows superior performance to several state-of-the-art tracking algorithms.