Journal
SENSORS
Volume 15, Issue 1, Pages 135-147Publisher
MDPI
DOI: 10.3390/s150100135
Keywords
sign language recognition; conditional random field; BoostMap embedding
Funding
- National Research Foundation of Korea - Korean Government [NRF-2011-013-D00097]
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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%.
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