Head pose estimation framework based on feature point detection

In order to improve the precision of head pose estimation with random regression forest, an analysis framework based on feature point recognition was proposed for head pose estimation. In view of invalid votes disturbance, this framework recognized head position point and direction point to avoid accepting abnormal voting. The decision function used depth value, normal vector, Gaussian curvature and mean curvature as image features. An approximate optimized decision function search was executed in a binary test pool generated randomly according to information gain of differential entropy. The experiments focused on performance analysis on different occlusion rates of original data. The approach got high success rate in experiments with appropriate parameters, improved accuracy after using curvature, and enhanced the capability of handling with occlusion. The proposed framework has been applied for real-time head pose estimation system in virtual cockpits successfully.