title: Sign language recognition with the Kinect sensor based on conditional random fields.
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%.
authors.affiliation: Department of Computer Engineering, Chosun University, Seosuk-dong, Dong-ku, Gwangju 501-759, Korea. heedeok_yang@chosun.ac.kr.
authors.forename: Hee-Deok
authors.initials: HD
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D005145:Face; D006225:Hand / Q000502:physiology; D006801:Humans; D009068:Movement; D010363:Pattern Recognition, Automated; D012813:Sign Language