Abstract

Emotion recognition from facial images is a very active re- search topic in human computer interaction (HCI). However, most of the previous approaches only focus on the frontal or nearly frontal view facial images. In contrast to the frontal/nearly-frontal view images, emo- tion recognition from non-frontal view or even arbitrary view facial im- ages is much more difficult yet of more practical utility. To handle the emotion recognition problem from arbitrary view facial images, in this paper we propose a novel method based on the regional covariance matrix (RCM) representation of facial images. We also develop a new discrimi- nant analysis theory, aiming at reducing the dimensionality of the facial feature vectors while preserving the most discriminative information, by minimizing an estimated multiclass Bayes error derived under the Gaus- sian mixture model (GMM). We further propose an efficient algorithm to solve the optimal discriminant vectors of the proposed discriminant anal- ysis method. We render thousands of multi-view 2D facial images from the BU-3DFE database and conduct extensive experiments on the gener- ated database to demonstrate the effectiveness of the proposed method. It is worth noting that our method does not require face alignment or facial landmark points localization, making it very attractive.