NDLI: Bayesian Sensing Hidden Markov Model for Hand Gesture Recognition

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Bayesian Sensing Hidden Markov Model for Hand Gesture Recognition

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Bayesian Sensing Hidden Markov Model for Hand Gesture Recognition

Content Provider	ACM Digital Library
Author	Santoso, Andri Hernawan, Ari Lee, Yuan-Shan Wang, Chien-Yao Wang, Jia-Ching
Abstract	This paper proposes a modified Bayesian Sensing Hidden Markov Model (BS-HMM) to address the problem of hand gestures recognition on few labeled data. In this work, BS-HMM is investigated based on its success to address the problem of large-vocabulary of continuous speech recognition. We introduced error modeling into BS-HMM basis vector to handle the noise that occurs in the data. We also introduced a forgetting factor to preserve important information from previous basis vector and to improve both convergence and representation ability of the BS-HMM basis vector. We modified Moving Pose method to extract the feature descriptor from hand gestures data. To evaluate the performance of our system, we compared our proposed method with previously proposed HMM methods. The experimental result showed the improvement of proposed method over others, even when only a small number of labeled data are available for training dataset.
Starting Page	1
Ending Page	5
Page Count	5
File Format	PDF
ISBN	9781450337359
DOI	10.1145/2818869.2818925
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Moving pose descriptor Bayesian sensing hidden markov models Hand gesture recognition
Content Type	Text
Resource Type	Article

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