A fusion method for robust face tracking

Abstract

Face tracking often encounters drifting problems, especially when a significant face appearance variation occurs. Many trackers suffer from the difficulty of facial feature extraction during a wide range of face turning, occlusion, and even invisibleness. In this paper, we propose a novel and efficient fusion strategy for robust face tracking. A Supervised Descent Method (SDM) and a Compressive Tracking method (CT) are employed at the same time. SDM is used to correct drifting errors of CT continuously during frontal face tracking. However, when the face orientation changes to the angle orthogonal to the view line, it results in tracking failure for the SDM method. CT is then adopted to keep the face region being tracked until SDM detects and tracks the face again. In the experiments, we test the proposed method for real-time tracking using several challenging sequences from recent literatures. The fusion strategy has achieved encouraging performance in terms of both efficiency and reliability.