A parameter-free locality sensitive discriminant analysis and its application to coarse 3D head pose estimation

Abstract

In this paper we propose a novel parameterless approach for discriminative analysis. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) while all spectral-graph based manifold learning techniques (supervised and unsupervised) are depending on several parameters that require manual tuning, ours is parameter-free, and (ii) it adaptively estimates the local neighborhood surrounding each sample based on the data similarity. Our approach has been applied to the problem of modeless coarse 3D head pose estimation. It was tested on two databases FacePix and Pointing'04. It was conveniently compared with other linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on coarse 3D head pose problem, the proposed approach could also be applied to other classification tasks for objects characterized by large variance in their appearance. © 2011 Springer-Verlag.