Head pose estimation using image abstraction and local directional quaternary patterns for multiclass classification

• Cartoon-like facial contour images can abstract properties of common facial poses.
• Our methods reduced the noise caused by variations in identity, facial expressions.
• Facial binary images delivered good results without any illumination compensation.
• LDQP method with image abstraction outperformed state of the art algorithms.

This study treats the problem of coarse head pose estimation from a facial image as a multiclass classification problem. Head pose estimation continues to be a challenge for computer vision systems because extraneous characteristics and factors that lack pose information can change the pixel values in facial images. Thus, to ensure robustness against variations in identity, illumination conditions, and facial expressions, we propose an image abstraction method and a new representation method (local directional quaternary patterns, LDQP), which can remove unnecessary information and highlight important information during facial pose classification. We verified the efficacy of the proposed methods in experiments, which demonstrated its effectiveness and robustness against different types of variation in the input images.