A novel feature descriptor based on biologically inspired feature for head pose estimation

This paper proposes a novel method to improve the accuracy of head pose estimation. Since biologically inspired features (BIF) have been demonstrated to be both effective and efficient for many visual tasks, we argue that BIF can be applied to the problem of head pose estimation. By combining the BIF with the well-known local binary pattern (LBP) feature, we propose a novel feature descriptor named "local biologically inspired features" (LBIF). Considering that LBIF is extrinsically very high dimensional, ensemble-based supervised methods are applied to reduce the dimension while at the same time improving its discriminative ability. Results obtained from the evaluation on two different databases show that the proposed LBIF feature achieves significant improvements over the state-of-the-art methods of head pose estimation. (C) 2013 Elsevier B.V. All rights reserved.