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Sensors 2015, 15(1), 135-147; doi:10.3390/s150100135

Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields

Department of Computer Engineering, Chosun University, Seosuk-dong, Dong-ku, Gwangju 501-759, Korea
Received: 4 September 2014 / Accepted: 19 December 2014 / Published: 24 December 2014
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
View Full-Text   |   Download PDF [753 KB, uploaded 31 December 2014]   |  

Abstract

Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D) space. In this research, we use 3D depth information from hand motions, generated from Microsoft’s Kinect sensor and apply a hierarchical conditional random field (CRF) that recognizes hand signs from the hand motions. The proposed method uses a hierarchical CRF to detect candidate segments of signs using hand motions, and then a BoostMap embedding method to verify the hand shapes of the segmented signs. Experiments demonstrated that the proposed method could recognize signs from signed sentence data at a rate of 90.4%. View Full-Text
Keywords: sign language recognition; conditional random field; BoostMap embedding sign language recognition; conditional random field; BoostMap embedding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, H.-D. Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields. Sensors 2015, 15, 135-147.

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