Welcome to the InfoSci Platform
Emotion Recognition from Facial Expression and Electroencephalogram Signals

Emotion Recognition from Facial Expression and Electroencephalogram Signals

Amit Konar, Aruna Chakraborty, Pavel Bhowmik, Sauvik Das, Anisha Halder
Copyright: © 2012 |Pages: 28
ISBN13: 9781613504291|ISBN10: 1613504292|EISBN13: 9781613504307
DOI: 10.4018/978-1-61350-429-1.ch017
Cite Chapter Cite Chapter

MLA

Konar, Amit, et al. "Emotion Recognition from Facial Expression and Electroencephalogram Signals." Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, edited by Vijay Kumar Mago and Nitin Bhatia, IGI Global, 2012, pp. 310-337. https://doi.org/10.4018/978-1-61350-429-1.ch017

APA

Konar, A., Chakraborty, A., Bhowmik, P., Das, S., & Halder, A. (2012). Emotion Recognition from Facial Expression and Electroencephalogram Signals. In V. Mago & N. Bhatia (Eds.), Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies (pp. 310-337). IGI Global. https://doi.org/10.4018/978-1-61350-429-1.ch017

Chicago

Konar, Amit, et al. "Emotion Recognition from Facial Expression and Electroencephalogram Signals." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, edited by Vijay Kumar Mago and Nitin Bhatia, 310-337. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-429-1.ch017

Export Reference

Mendeley
Favorite

Abstract

This chapter proposes new approaches to emotion recognition from facial expression and electroencephalogram signals. Subjects are excited with selective audio-visual stimulus, responsible for arousal of specific emotions. Manifestation of emotion which appears in facial expression and EEG are recorded. Subsequently the recorded information is analyzed for extraction of features, and a support vector machine classifier is used to classify the extracted features into emotion classes. An alternative scheme for emotion recognition directly from the electroencephalogram signals using Duffing Oscillator is also presented. Experimental results are given to compare the relative merits of the proposed schemes with existing works.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.