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A Human Affect Recognition System for Socially Interactive Robots

A Human Affect Recognition System for Socially Interactive Robots

Derek McColl, Goldie Nejat
Copyright: © 2013 |Pages: 20
ISBN13: 9781466622111|ISBN10: 1466622113|EISBN13: 9781466622128
DOI: 10.4018/978-1-4666-2211-1.ch029
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MLA

McColl, Derek, and Goldie Nejat. "A Human Affect Recognition System for Socially Interactive Robots." Handbook of Research on Technoself: Identity in a Technological Society, edited by Rocci Luppicini, IGI Global, 2013, pp. 554-573. https://doi.org/10.4018/978-1-4666-2211-1.ch029

APA

McColl, D. & Nejat, G. (2013). A Human Affect Recognition System for Socially Interactive Robots. In R. Luppicini (Ed.), Handbook of Research on Technoself: Identity in a Technological Society (pp. 554-573). IGI Global. https://doi.org/10.4018/978-1-4666-2211-1.ch029

Chicago

McColl, Derek, and Goldie Nejat. "A Human Affect Recognition System for Socially Interactive Robots." In Handbook of Research on Technoself: Identity in a Technological Society, edited by Rocci Luppicini, 554-573. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2211-1.ch029

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Abstract

This chapter presents a real-time robust affect classification methodology for socially interactive robots engaging in one-on-one human-robot-interactions (HRI). The methodology is based on identifying a person’s body language in order to determine how accessible he/she is to a robot during the interactions. Static human body poses are determined by first identifying individual body parts and then utilizing an indirect 3D human body model that is invariant to different body shapes and sizes. The authors implemented and tested their technique using two different sensory systems in social HRI scenarios to motivate its robustness for the proposed application. In particular, the experiments consisted of integrating the proposed body language recognition and affect classification methodology with imaging-based sensory systems onto the human-like socially interactive robot Brian 2.0 in order for the robot to recognize affective body language during one-on-one interactions.

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