Robust face tracking using multiple appearance models and graph relational learning

摘要

This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios. In this paper, FaceTrack is proposed that utilizes multiple appearance models with its long-term and short-term appearance memory for efficient face tracking. It demonstrates robustness to deformation, in-plane and out-of-plane rotation, scale, distractors and background clutter. It integrates on the advantages of the tracking-by-detection by using a face detector that tackles the drastic scale appearance change of a face. A weighted score-level fusion strategy is proposed to obtain the face tracking output having the highest fusion score by generating candidates around possible face locations. FaceTrack showcases impressive performance when initiated automatically by outperforming several state-of-the-art trackers, except Struck by a very minute margin: 0.001 in precision and 0.017 in success, respectively.