Spatial and probabilistic codebook template based head pose estimation from unconstrained environments

In unconstrained environments, head pose detection can be very challenging due to the joint and arbitrary occurrence of facial expressions, background clutter, partial occlusions and illumination conditions. Despite the wide range of head pose literature, most current methods can address this problem only up to a certain degree, and mostly for restricted scenarios. In this paper, we address the problem of head pose classification from real world images with large appearance variation. We represent each pose with a probabilistic and spatial template learned from facial codewords. The inference of the best template representing a test image is achieved probabilistically and spatially at the codebook. The experimental results are obtained from 5500 video frames collected under different illumination and background conditions. Our probabilistic framework is shown to outperform the current state-of-the-art in head pose classification.