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A Computer Vision-Based Gesture Recognition Using Hidden Markov Model

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Innovations in Soft Computing and Information Technology

Abstract

One of the unique features present in the human being is a gesture. Nowadays, the human gesture is used to interact with the computer which will reduce the workload. The gesture recognition consists of various types of human interfaces such as facial, finger, iris, hand. In this paper, we proposed a hand gesture recognition using hidden Markov model (HMM) and trajectory code word. There are various papers presented on this topic by using different methods, whereas our technique uses the four phases for recognition: (i) tracking of real-time gesture and segmentation, (ii) feature extraction using the Gaussian model, (iii) HMM likelihood for forward algorithm, and (iv) gesture recognition using Viterbi decoding. Because of using the inbuilt function of OpenCV and MATLAB, it would be very easy to develop the system. The experimental results show the accuracy of 99%, in recognition rate, whereas it is quite faster while using for real-time application.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Keshav Sinha, Rashmi Kumari, Annu Priya & Partha Paul

Authors
  1. Keshav Sinha
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  2. Rashmi Kumari
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  3. Annu Priya
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  4. Partha Paul
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Corresponding author

Correspondence to Keshav Sinha .

Editor information

Editors and Affiliations

  1. Department of Chemistry, Amity University, Ranchi, Jharkhand, India

    Jayeeta Chattopadhyay

  2. Department of Mechanical Engineering, Amity University, Ranchi, Jharkhand, India

    Rahul Singh

  3. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Vandana Bhattacherjee

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Sinha, K., Kumari, R., Priya, A., Paul, P. (2019). A Computer Vision-Based Gesture Recognition Using Hidden Markov Model. In: Chattopadhyay, J., Singh, R., Bhattacherjee, V. (eds) Innovations in Soft Computing and Information Technology . Springer, Singapore. https://doi.org/10.1007/978-981-13-3185-5_6

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  • DOI: https://doi.org/10.1007/978-981-13-3185-5_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3184-8

  • Online ISBN: 978-981-13-3185-5

  • eBook Packages: EngineeringEngineering (R0)

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