  Feature extraction analysis has been widely investigated during the last
decades in computer vision community due to the large range of possible
applications. Significant work has been done in order to improve the
performance of the emotion detection methods. Classification algorithms have
been refined, novel preprocessing techniques have been applied and novel
representations from images and videos have been introduced. In this paper, we
propose a preprocessing method and a novel facial landmarks' representation
aiming to improve the facial emotion detection accuracy. We apply our novel
methodology on the extended Cohn-Kanade (CK+) dataset and other datasets for
affect classification based on Action Units (AU). The performance evaluation
demonstrates an improvement on facial emotion classification (accuracy and F1
score) that indicates the superiority of the proposed methodology.
