Three-Stage Model for Robust Real-Time Face Tracking

摘要

We propose a novel face tracking framework, the three-stage model, for robust face tracking against interruptions from face-like blobs. For robust face tracking in real-time, we considered two critical factors in the construction of the proposed model. One factor is the exclusion of background information in the initialization of the target model, the extraction of the target candidate region, and the updating of the target model. The other factor is the robust estimation of face movement under various environmental conditions. The proposed three-stage model consists of a preattentive stage, an assignment stage, and a postattentive stage with a prerecognition phase. The model is constructed by means of effective integration of optimum cues that are selected in consideration of the trade-off between true positives and false positives of face classification based on a context-dependant type of categorization. The experimental results demonstrate that the proposed tracking method improves the performance of the real-time face tracking process in terms of success rates and with robustness against interruptions from face-like blobs.