Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Adaptive Real-Time Emotion Recognition from Body Movements

Adaptive Real-Time Emotion Recognition from Body Movements Adaptive Real-Time Emotion Recognition from Body Movements WEIYI WANG and VALENTIN ENESCU, AVSP-ETRO, Vrije Universiteit Brussel (VUB) HICHEM SAHLI, AVSP-ETRO, Vrije Universiteit Brussel (VUB) and Interuniversity Microelectronics Center (IMEC) 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. CCS Concepts: http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Adaptive Real-Time Emotion Recognition from Body Movements

Download PDF
21 pages

Loading next page...
 
/lp/association-for-computing-machinery/adaptive-real-time-emotion-recognition-from-body-movements-yA7CtaY1OM

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2738221
Publisher site
See Article on Publisher Site

Abstract

Adaptive Real-Time Emotion Recognition from Body Movements WEIYI WANG and VALENTIN ENESCU, AVSP-ETRO, Vrije Universiteit Brussel (VUB) HICHEM SAHLI, AVSP-ETRO, Vrije Universiteit Brussel (VUB) and Interuniversity Microelectronics Center (IMEC) 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. CCS Concepts:

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS) – Association for Computing Machinery

Published: Dec 22, 2015

There are no references for this article.