Integrating multiple visual cues for robust real-time 3D face tracking

Authors:
Wei-Kai Liao;Douglas Fidaleo;Gérard Medioni
Affiliations:
Computer Science Department, Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA;Computer Science Department, Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA;Computer Science Department, Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA
Venue:
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Year:
2007

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Abstract

3D face tracking is an important component for many computer vision applications. Most state-of-the-art tracking algorithms can be characterized as being either intensity- or feature-based. The intensity-based tracker relies on the brightness constraint while the feature-based tracker utilizes 2D local feature correspondences. In this paper, we propose a hybrid tracker for robust 3D face tracking. Instead of relying on single source of information, the hybrid tracker integrates feature correspondence and brightness constraints within a nonlinear optimization framework. The proposed method can track the 3D face pose reliably in real-time. We have conducted a series of evaluations to compare the performance of the proposed tracker with other state-of-the-art trackers. The experiments consist of synthetic sequences with simulation of different environmental factors, real sequences with estimated ground truth, and sequences from a real-world HCI application. The proposed tracker is shown to be superior in both accuracy and robustness.