Presentation 2014-01-24
Sign Language Recognition of Different Hand Shape in the Same Arm Motion
Daisuke IMAMURA, Yoshihiro FURUYA, Yasuo HORIUCHI, Kazuhiko KAWAMOTO, Shingo KUROIWA,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this paper, we will introduce a continuous sign language recognition method which can distinguish the words with the same arm motion and the different hand shape. We have proposed a sign language recognition method based on the Hidden Markov Model tracking the signer's arm motion. However, the method used only arm motion and it was unable to distinguish the words with the different hand shape and the same arm motion. In this study, the hand shape images were extracted when the arm motion stopped or the movement direction of arm changes significantly. The extracted images are classified by the Support Vector Machine and identified as the proper sign word. As the result of the recognition experiment, the recognition accuracy was about 80% for the words with the different hand shape.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Sign Language / Histogram of Oriented Gradients / Support Vector Machine / Motion Sensor
Paper # PRMU2013-117,MVE2013-58
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Conference Information
Committee MVE
Conference Date 2014/1/16(1days)
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Registration To Media Experience and Virtual Environment (MVE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sign Language Recognition of Different Hand Shape in the Same Arm Motion
Sub Title (in English)
Keyword(1) Sign Language
Keyword(2) Histogram of Oriented Gradients
Keyword(3) Support Vector Machine
Keyword(4) Motion Sensor
1st Author's Name Daisuke IMAMURA
1st Author's Affiliation Chiba University()
2nd Author's Name Yoshihiro FURUYA
2nd Author's Affiliation Chiba University
3rd Author's Name Yasuo HORIUCHI
3rd Author's Affiliation Chiba University
4th Author's Name Kazuhiko KAWAMOTO
4th Author's Affiliation Chiba University
5th Author's Name Shingo KUROIWA
5th Author's Affiliation Chiba University
Date 2014-01-24
Paper # PRMU2013-117,MVE2013-58
Volume (vol) vol.113
Number (no) 403
Page pp.pp.-
#Pages 6
Date of Issue