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dc.contributor.authorSong, KTen_US
dc.contributor.authorChen, WJen_US
dc.date.accessioned2014-12-08T15:25:46Z-
dc.date.available2014-12-08T15:25:46Z-
dc.date.issued2004en_US
dc.identifier.isbn0-7803-8566-7en_US
dc.identifier.issn1062-922Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/18196-
dc.description.abstractThis paper presents a design and experimental study of human-robot interaction via face recognition and image tracking. A new architecture is proposed for fast face recognition of family members. In the proposed system, each family member has his/her own RBF neural networks. Each neural network is only responsible for recognizing its trained member. Consequently, the database is small and the processing time required for face recognition is minimized. A recognition rate of 94% has been achieved, an improvement relative to conventional approaches. In order to detect and track a person, we also developed an algorithm for detecting multiple faces in a scene based on division of skin and hair color regions. The face recognition and image tracking system has been integrated to an experimental mobile robot. Practical experiments reveal that the robot demonstrates real-time face recognition and tracking performance.en_US
dc.language.isoen_USen_US
dc.subjectface recognitionen_US
dc.subjecthome robotsen_US
dc.subjectvisual trackingen_US
dc.subjectRBF neural networksen_US
dc.titleFace recognition and tracking for human-robot interactionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7en_US
dc.citation.spage2877en_US
dc.citation.epage2882en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000226863300482-
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