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http://dx.doi.org/10.5392/JKCA.2013.13.11.028

Hand Shape Detection and Recognition using Self Organized Feature Map(SOMF) and Principal Component Analysis  

Kim, Kyoung-Ho (조선대학교 전산통계학과)
Lee, Kee-Jun (광주보건대학교 병원전산관리과)
Publication Information
The Journal of the Korea Contents Association / v.13, no.11, 2013 , pp. 28-36 More about this Journal
Abstract
This study proposed a robust detection algorithm. It detects hands more stably with respect to changes in light and rotation for the identification of a hand shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the hand area through pre-processing using a hand shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a hand area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the hand shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.
Keywords
Self Organized Feature Map(SOFM); Principal Component Analysis; Recognition of Hand Shape;
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