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Bayesian Sensing Hidden Markov Model for Hand Gesture Recognition

Published: 07 October 2015 Publication History

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.

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  • (2022)Recognition of Real-Time Hand Gestures using Mediapipe Holistic Model and LSTM with MLP Architecture2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC55597.2022.10001800(292-295)Online publication date: 17-Dec-2022

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cover image ACM Other conferences
ASE BD&SI '15: Proceedings of the ASE BigData & SocialInformatics 2015
October 2015
381 pages
ISBN:9781450337359
DOI:10.1145/2818869
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Association for Computing Machinery

New York, NY, United States

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Published: 07 October 2015

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  1. Bayesian Sensing Hidden Markov Models
  2. Hand Gesture Recognition
  3. Moving Pose Descriptor

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ASE BD&SI '15
ASE BD&SI '15: ASE BigData & SocialInformatics 2015
October 7 - 9, 2015
Kaohsiung, Taiwan

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  • (2022)Recognition of Real-Time Hand Gestures using Mediapipe Holistic Model and LSTM with MLP Architecture2022 IEEE 10th Conference on Systems, Process & Control (ICSPC)10.1109/ICSPC55597.2022.10001800(292-295)Online publication date: 17-Dec-2022

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