A study on vision-based robust hand-posture recognition by learning similarity between hand-posture and structure

Jang Hyoyoung, Jung Jin-Woo, Bien Zeungnam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a robust hand-posture recognition method by learning similarity between hand-posture and structure for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the user's hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of user's hand restricts the user's freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and includes learning the similarity between the two types of features. The validity of the proposed method is evaluated by applying it to the hand-posture recognition system using three cameras.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings
PublisherSpringer Verlag
Pages550-559
Number of pages10
ISBN (Print)3540459073, 9783540459071
StatePublished - 2006
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 24 Sep 200628 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4222 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Natural Computation, ICNC 2006
Country/TerritoryChina
CityXi'an
Period24/09/0628/09/06

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