Please use this identifier to cite or link to this item:
|Title:||Coupled HMM-based multi-sensor data fusion for sign language recognition|
Hidden Markov model (Coupled HMM, HMM)
Sign language recognition
|Abstract:||Recent development of low cost depth sensors such as Leap motion controller and Microsoft kinect sensor has opened up new opportunities for Human-Computer-Interaction (HCI). In this paper, we propose a novel multi-sensor fusion framework for Sign Language Recognition (SLR) using Coupled Hidden Markov Model (CHMM). CHMM provides interaction in state-space instead of observation states as used in classical HMM that fails to model correlation between inter-modal dependencies. The framework has been used to recognize dynamic isolated sign gestures performed by hearing impaired persons. The dataset has been tested using existing data fusion approaches. The best recognition accuracy has been achieved as high as 90.80% with CHMM. Our CHMM-based approach shows improvement in recognition performance over popular existing data fusion techniques. � 2016 Elsevier B.V.|
|Appears in Collections:||Research Publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.