Maximum Margin Discriminant Projections for facial expression recognition

We present a novel dimensionality reduction method which aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated and separated with maximum margin. The proposed method brings certain advantages, both to data embedding and classification. It improves classification performance, reduces the required training time of the SVM classifier, since it is trained over the projected low dimensional samples and also data outliers and the overall data samples distribution inside classes do not affect its performance. The proposed method has been applied for facial expression recognition in Cohn-Kanade database verifying its superiority in this task, against other state-of-the-art dimensionality reduction techniques.