Synchronized Submanifold Embedding for Robust and Real-Time Capable Head Pose Detection Based on Range Images

Abstract

Automatic head pose estimation plays an important part in the development of human machine interfaces. This paper proposes a fast and frugal method for accurate and person-independent head pose estimation based on range images. Head pose estimation is treated as a non-linear regression problem and addressed with Synchronized Submanifold Embedding (SSE). The offline training step exploits the local linear structure of label and feature space for a cross-wise synchronization of pose samples from different subjects. Based on this, Multi-class Linear Discriminant Analysis (M-LDA) identifies a dimensionality-reducing linear projection, which diminishes non head pose related information. New samples are then projected into this lower dimensional feature space and classified based on training samples within their local neighborhood. In case of sequential data, the occurrence of outliers can be reduced using a reasonable preselection of neighborhood candidates based on tracking of pose changes. The experimental results on a publicly available database prove that the proposed algorithm can handle a large range of pose changes and outperforms existing methods in accuracy.