Emotion Recognition From Body Movement

• Main Authors: Ferdous Ahmed, A. S. M. Hossain Bari, Marina L. Gavrilova
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Emotion recognition; feature selection; gait analysis; genetic algorithm; information fusion; human motion
• Online Access: https://ieeexplore.ieee.org/document/8945309/

Automatic emotion recognition from the analysis of body movement has tremendous potential to revolutionize virtual reality, robotics, behavior modeling, and biometric identity recognition domains. A computer system capable of recognizing human emotion from the body can also significantly change the way we interact with the computers. One of the significant challenges is to identify emotion-specific features from a vast number of descriptors of human body movements. In this paper, we introduce a novel two-layer feature selection framework for emotion classification from a comprehensive list of body movement features. We used the feature selection framework to accurately recognize five basic emotions: happiness, sadness, fear, anger, and neutral. In the first layer, a unique combination of Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) was utilized to eliminate irrelevant features. In the second layer, a binary chromosome-based genetic algorithm was proposed to select a feature subset from the relevant list of features that maximizes the emotion recognition rate. Score and rank-level fusion were applied to further improve the accuracy of the system. The proposed system was validated on proprietary and public datasets, containing 30 subjects. Different action scenarios, such as walking and sitting actions, as well as an action-independent case, were considered. Based on the experimental results, the proposed emotion recognition system achieved a very high emotion recognition rate outperforming all of the state-of-the-art methods. The proposed system achieved recognition accuracy of 90.0% during walking, 96.0% during sitting, and 86.66% in an action-independent scenario, demonstrating high accuracy and robustness of the developed method.