Two-Layer Feature Selection Algorithm for Recognizing Human Emotions from 3D Motion Analysis

Abstract

Research on automatic recognition of human emotion from motion is gaining momentum, especially in the areas of virtual reality, robotics, behavior modeling, and biometric identity recognition. One of the challenges is to identify emotion-specific features from a vast number of expressive descriptors of human motion. In this paper, we have developed a novel framework for emotion classification using motion features. We combined a filter-based feature selection algorithm and a genetic algorithm to recognize four basic emotions: Happiness, sadness, fear, and anger. The validity of the proposed framework was confirmed on a dataset containing 30 subjects performing expressive walking sequences. Our proposed framework achieved a very high recognition rate outperforming existing state-of-the-art methods in the literature.