Subspace learning for human head pose estimation

This paper proposes a fully automatic framework for static human head pose estimation. With a 2D human multi-view face image as input, the face region is detected and cropped out. Then the pose of the face is assessed by the pose categories. Based on the appearance of the face region, variant subspace learning methods including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP) and pose-specific subspace (PSS) are proposed for effective representation of the face poses. Several aspects, such as human identification, illumination changes and expression variations are considered during the classification process. The experiment results on large public database demonstrate the effectiveness of the proposed framework and recognition algorithms. Performance comparisons and discussions are also provided in detail to help the algorithm selection when designing practical face pose estimation systems for different scenarios.