Manifold based methods in facial expression recognition

This paper describes a novel method for facial expression recognition based on non-linear manifold techniques. The graph-based algorithms are designed to treat structure in data, and regularize accordingly. This same goal is shared by several other algorithms, from linear method principal components analysis (PCA) to modern variants such as Laplacian eigenmaps. In this paper we focus on manifold learning for dimensionality reduction and clustering using Laplacian eigenmaps for facial expression recognition. We evaluate the algorithm by using all the pixels and selected features respectively and compare the performance of the proposed non-linear manifold method with the previous linear manifold approach, and the non linear method produces higher recognition rate than the facial expression representation using linear methods.