Linear regression for head pose analysis

Extensive research has been conducted to estimate and analyze head poses for various applications. Most existing methods tend to detect facial features and locate landmarks on a face for pose estimation. However, the sensitivity to occlusion of some face parts with key features and uncontrolled illumination of face images make the facial feature detection vulnerable. In this paper, we propose a framework for pose estimation without the need of face features or landmarks detection. Specifically, we formulate the pose estimation as a linear regression applied to the pose space. This method is based on the assumption that pose space cannot be linearly approximated in the pose subspace. The experimental results strongly support this assumption. In cases where the database does not obtain various poses in the intraclass, we propose to generate those poses through a 3D reconstruction and projection method. The experiment conducted on the CMU MultiPIE and IMM Face database has shown the effectiveness of the proposed method.