Head pose estimation based on Active Shape Model and Relevant Vector Machine

Abstract

Human head pose estimation is a hot topic in computer vision field, which can be used in video surveillance, Human Computer Interaction and so on. Active Shape Model is a template matching method, which is suitable for object localization and point based feature extraction. In this paper, we propose an algorithm based on Active Shape Model for head pose estimation. In the proposed algorithm, we firstly use Active Shape Model to estimate 2D face feature points of the target human head, then we adopt Relevant Vector Machine to evaluate head pose based on the extracted feature points. Experiments on CAS-PEAL-R1 dataset show that the proposed algorithm has great potential in estimating head pose with small yaw angle.