A Model-Based Particle Filter for 3D Head Pose Estimation

• Main Authors: Nian-Tzu Gau, 高念慈
• Other Authors: Yuan-Kai Wang
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/25493622000975258156

碩士 === 輔仁大學 === 電機工程學系 === 99 === Head pose estimation is a technique that determinate the orientation of face. The orientation of human face is a important information, face is a significant symbol that show human attention and behavior. For estimating the pose of head, tracking the feature points on face is very important. Particle filter is a tracking algorithm that alternative of extend Kalman filter, it has been widely used for solving tracking problem. It predict a moving object location from observation value that contains noises. In this paper, we propose a model-based particle filter that tracks the feature point on the face and fits by AAM. the proposed model-base particle filter that use non-linear regression analysis to train a state transition model to make the state transition more efficiently. The experimental result show that model-based particle filter have better head pose estimation than classic particle filter.