A binary decision tree based real-time emotion detection system

Authors:
Adam Livingston;Ming-Jung Seow;Vijayan K. Asari
Affiliations:
Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA;Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA;Computational Intelligence and Machine Vision Laboratory, Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Year:
2007

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Abstract

This paper presents a real-time emotion detection system capable of identifying seven affective states: agreeing, concentrating, disagreeing, interested, thinking, unsure, and angry from a near infrared video stream. A Viola Jones face detector is trained to locate the face within the frame. The Active Appearance Model is then used to place 23 landmark points around key areas of the eyes, brows, and mouth. A prioritized binary decision tree then detects, based on the actions of these key points, if on of the seven emotional states occurs as frames pass. The completed system runs accurately and seamlessly on an Intel Pentium IV, 2.8 GHz processor with 512 MB of memory, achieving a real-time frame rate of 36 frames per second.