Head pose estimation using motion subspace matching on GPU

The head pose estimation is a process of recovering 3D head position in term of yaw, pitch and roll from 2D images. However, the reduction of information from 3D to 2D leads to an ill-posed problem. In this paper, we propose a novel algorithm of head pose estimation that includes facial features tracking for Thai sign language recognition. In order to estimate head pose correctly, feature points tracking requires high precision. Nevertheless it is difficult for low cost cameras where input image quality may be generally poor. To overcome this problem, we introduce an automatic camera signal calibration such that the features can be tracked correctly despite the quality of the input image sequences. Finally, as our approach bases on the state space searching, the local minima problem is common. Hence, we divide the search space into sub spaces and perform parallel computation on GPU.