Adaptive Real-Time Emotion Recognition from Body Movements

Abstract

We propose a real-time system that continuously recognizes emotions from body movements. The combined low-level 3D postural features and high-level kinematic and geometrical features are fed to a Random Forests classifier through summarization (statistical values) or aggregation (bag of features). In order to improve the generalization capability and the robustness of the system, a novel semisupervised adaptive algorithm is built on top of the conventional Random Forests classifier. The MoCap UCLIC affective gesture database (labeled with four emotions) was used to train the Random Forests classifier, which led to an overall recognition rate of 78% using a 10-fold cross-validation. Subsequently, the trained classifier was used in a stream-based semisupervised Adaptive Random Forests method for continuous unlabeled Kinect data classification. The very low update cost of our adaptive classifier makes it highly suitable for data stream applications. Tests performed on the publicly available emotion datasets (body gestures and facial expressions) indicate that our new classifier outperforms existing algorithms for data streams in terms of accuracy and computational costs.