An Investigation of Model Bias in 3D Face Tracking

3D tracking of faces in video streams is a difficult problem that can be assisted with the use of a priori knowledge of the structure and appearance of the subject’s face at predefined poses (keyframes). This paper provides an extensive analysis of a state-of-the-art keyframebased tracker: quantitatively demonstrating the dependence of tracking performance on underlying mesh accuracy, number and coverage of reliably matched feature points, and initial keyframe alignment. Tracking with a generic face mesh can introduce an erroneous bias that leads to degraded tracking performance when the subject’s out-of-plane motion is far from the set of keyframes. To reduce this bias, we show how online refinement of a rough estimate of face geometry may be used to re-estimate the 3d keyframe features, thereby mitigating sensitivities to initial keyframe inaccuracies in pose and geometry. An in-depth analysis is performed on sequences of faces with synthesized rigid head motion. Subsequent tri...

• Type: Conference
• Year: 2005
• Where: AMFG
• Authors: Douglas Fidaleo, Gérard G. Medioni, Pascal Fua, Vincent Lepetit
• Keywords: AMFG 2005, Biometrics, Initial Keyframe, Initial Keyframe Alignment, Initial Keyframe Inaccuracies