Fast head pose estimation using depth data

Abstract

In order to estimate head pose precisely in real time with computer vision technology, an enhanced framework using depth data and random regression forest is implemented for head pose estimation. This framework bases on head position and direction point recognition to accomplish head pose estimation. When training random forest, a decision function derived from Haar-like features is used as the binary test and this test uses some data features like Gaussian Curvature and Mean Curvature besides depth value and normal vector. We also generate a large training dataset of range images of heads by virtual structured light scanning. All votes of patches are filtered by clustering and mean shift, and then mean of them are used to estimate position of feature points. Performance evaluation shows accurate pose estimation (success rate above 90%) when running at real-time speed.