A fast and robust feature set for cross individual facial expression recognition

Abstract

This paper presents a new simple and robust set of features to classify emotional states in sequences of facial images. The proposed method is derived from simple geometric-based features that deliver a fast, highly discriminative, low-dimensional, and robust classification across individuals. The proposed method was compared to other state-of-the-art methods such as Gabor, LBP and AAM-based features. They were all compared using four different classifiers and experimental results based on these classifiers have shown that the proposed features are more stable in leave-same-sequence-image-out (LSSIO) environments, less computational intense and faster when compared to others. © 2012 Springer-Verlag Berlin Heidelberg.