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.
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