Using subclasses in discriminant non-negative subspace learning for facial expression recognition

Non-negative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. To achieve an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance, we regard that data inside each class form clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method combines these discriminant criteria as constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space. The developed algorithm has been applied to the facial expression recognition problem and experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.