Feature Reduction for Dimensional Emotion Recognition in Human-Robot Interaction

The introduction of social robots in human living spaces has brought to attention the need for robots to be equipped with emotion recognition capabilities to facilitate natural and social human-robot interactions. This paper explores the recognition of continuous dimensional emotion from facial expressions. It further investigates the use of principal component analysis (PCA), locality preserving projections (LPP) and factor analysis (FA) for reduction of the many features that are typically produced by facial feature extraction algorithms. The reduced features sets are modelled using Nonlinear AutoRegressive with eXogenous inputs Recurrent Neural Networks (NARX-RNN). The results show that PCA significantly outperfoms both LPP and FA techniques, and that the NARX-RNN model is a powerful predictor of continuous emotion.