期刊
PATTERN RECOGNITION LETTERS
卷 86, 期 -, 页码 1-8出版社
ELSEVIER
DOI: 10.1016/j.patrec.2016.12.004
关键词
Sign language recognition; Depth sensors; Hidden Markov model (Coupled HMM, HMM); Bayesian classification
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. (C) 2016 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据