Lang, Christian and Wachsmuth, Sven and Hanheide, Marc and Wersing, Heiko (2012) Facial communicative signals: valence recognition in task-oriented human-robot interaction. International Journal of Social Robotics, 4 (3). pp. 249-262. ISSN 1875-4791

PDF Lang2012-Facial_Communicative_Signals_-_Valence_Recognition_in_Task-Oriented_Human-Robot_Interaction.pdf - Whole Document Restricted to Repository staff only Download (820Kb)

Abstract

From the issue entitled "Measuring Human-Robots Interactions" This paper investigates facial communicative signals (head gestures, eye gaze, and facial expressions) as nonverbal feedback in human-robot interaction. Motivated by a discussion of the literature, we suggest scenario-specific investigations due to the complex nature of these signals and present an object-teaching scenario where subjects teach the names of objects to a robot, which in turn shall term these objects correctly afterwards. The robot’s verbal answers are to elicit facial communicative signals of its interaction partners. We investigated the human ability to recognize this spontaneous facial feedback and also the performance of two automatic recognition approaches. The first one is a static approach yielding baseline results, whereas the second considers the temporal dynamics and achieved classification rates

Item Type:	Article
Additional Information:	From the issue entitled "Measuring Human-Robots Interactions" This paper investigates facial communicative signals (head gestures, eye gaze, and facial expressions) as nonverbal feedback in human-robot interaction. Motivated by a discussion of the literature, we suggest scenario-specific investigations due to the complex nature of these signals and present an object-teaching scenario where subjects teach the names of objects to a robot, which in turn shall term these objects correctly afterwards. The robot’s verbal answers are to elicit facial communicative signals of its interaction partners. We investigated the human ability to recognize this spontaneous facial feedback and also the performance of two automatic recognition approaches. The first one is a static approach yielding baseline results, whereas the second considers the temporal dynamics and achieved classification rates
Keywords:	Robotics, Human-robot interaction
Subjects:	H Engineering > H670 Robotics and Cybernetics
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Marc Hanheide
Date Deposited:	12 Oct 2012 11:53
Last Modified:	13 Mar 2013 09:16
URI:	http://eprints.lincoln.ac.uk/id/eprint/6561

Actions (login required)

View Item