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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/1524
Title: Coupled HMM-based multi-sensor data fusion for sign language recognition
Authors: Kumar P.
Gauba H.
Roy P.P.
Dogra D.P.
Keywords: Bayesian classification
Depth sensors
Hidden Markov model (Coupled HMM, HMM)
Sign language recognition
Issue Date: 2017
Citation: 54
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.
URI: http://dx.doi.org/10.1016/j.patrec.2016.12.004
http://10.10.32.48:8080/jspui/handle/2008/1524
Appears in Collections:Research Publications

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