Driver’s head pose estimation using a hierarchical classification on an effective feature space

This paper introduces a direct accountability technique for the real-time estimation of the head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in three-dimensional space, our proposed approach based on a hierarchical expert classification will estimate the head pose for real-time applications, specifically for driver assistance systems. We proposed a method for feature extraction which aims at keeping only the angle-related features, independent of identity. We then used a hierarchical algorithm for classification which divides the whole space into smaller spaces. Moreover, we made a performance study aiming at evaluating the accuracy of the proposed approach. Experimental results show that the proposed features are able to describe the pose of the driver face image successfully, with a performance in agreement with the actual poses. This approach is not only useful for automobile safety and security but also can help severely handicapped people and can be used in collision prevention systems and other driving applications.