Classification of facial expressions by sparse coding
Expression variations in facial images is one of the most crucial and difficult problems in face-based computer vision applications. Although numerous systems have been proposed for robustness against facial expressions, so far it still persists to be an open problem.Considering that the knowledge on the type of the expression in a facial image would greatly facilitate the solution of this issue, in this paper we present an analysis for facial expressions classification in 2D frontal views. With the motivation of the success that sparse coding achieved in face recognition, similar principals are applied for to both original and dimension-reduced (via PCA) images and the resulting codes are classified based on two different approaches: minimum residual error and maximum interclass summation of the coefficients. Extensive tests are conducted on Bosphorus database, in which different expressions are available for 105 persons.