Linear Subspace Learning for Facial Expression Analysis
Caifeng Shan · BiblioBoard Library Catalog (Open Research Library) · 2009
In this chapter, we review and evaluate a number of linear subspace methods in the context of automatic facial expression analysis, which included recently proposed LPP, SLPP, OLPP, ONPP, LSDA, and the traditional PCA and LDA. These techniques are compared using different facial feature representations on several databases. Our experiments demonstrate that the supervised LPP performs best in modeling the underlying facial expression subspace resulting in the best expression recognition performance. We believe that this study is helpful and necessary for further research in linear subspace methods and facial expression analysis. It is believed that images of facial expressions lies on a non-linear low-dimensional manifold. Therefore, although linear subspace learning methods have been shown to be effective , non-linear manifold learning could potentially perform better for modeling facial expression space. For future work, we would expect to see research on discriminant nonlinear manifold learning techniques for facial expression analysis.