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Open AccessArticle

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
Sensors 2015, 15(1), 135-147; https://doi.org/10.3390/s150100135
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)
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
MDPI and ACS Style

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

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