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%.
Yang in REF presented hierarchical conditional random fields applied for detection of signs' segments and verified hand shapes of the segmented signs using BoostMap.
Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Medicine Computer Science