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Proceedings Paper

Person identification for mobile robot using audio-visual modality
Author(s): Young-Ouk Kim; Sehoon Chin; Jihoon Lee; Joonki Paik
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Paper Abstract

Recently, we experienced significant advancement in intelligent service robots. The remarkable features of an intelligent robot include tracking and identification of person using biometric features. The human-robot interaction is very important because it is one of the final goals of an intelligent service robot. Many researches are concentrating in two fields. One is self navigation of a mobile robot and the other is human-robot interaction in natural environment. In this paper we will present an effective person identification method for HRI (Human Robot Interaction) using two different types of expert systems. However, most of mobile robots run under uncontrolled and complicated environment. It means that face and speech information can't be guaranteed under varying conditions, such as lighting, noisy sound, orientation of a robot. According to a value of illumination and signal to noise ratio around mobile a robot, our proposed fuzzy rule make a reasonable person identification result. Two embedded HMM (Hidden Marhov Model) are used for each visual and audio modality to identify person. The performance of our proposed system and experimental results are compared with single modality identification and simply mixed method of two modality.

Paper Details

Date Published: 24 October 2005
PDF: 11 pages
Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 60060G (24 October 2005); doi: 10.1117/12.630337
Show Author Affiliations
Young-Ouk Kim, Chung-Ang Univ. (South Korea)
Korea Electronics Technology Institute (South Korea)
Sehoon Chin, Kwangwoon Univ. (South Korea)
Jihoon Lee, Chung-Ang Univ. (South Korea)
Joonki Paik, Chung-Ang Univ. (South Korea)


Published in SPIE Proceedings Vol. 6006:
Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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