Fuzzy discriminant projections for facial expression recognition

A linear projective map called fuzzy discriminant projections has been proposed in this paper. Fuzzy discriminant projection (FDP) is motivated by locality preserving projections which can optimally preserve the neighborhood structure of the data set. FDP utilizes the soft assignment method to weight pairs of samples with membership degree, and tries to find the optimal projective directions by maximizing the ratio of between-class distance against within-class distance. The resulting embedding subspace has more discriminant and robust power than that of traditional methods. Experiments on Cohn-Kanade databases show that FDP can effectively distinct the confusing facial expressions and obtain higher recognition accuracies than other subspacebased methods.