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Hand Gesture Identification Using Deep Learning and Artificial Neural Networks: A Review

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Computational Intelligence for Engineering and Management Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 984))

Abstract

Any human–computer interaction application needs to be able to recognize gestures. Hand gesture detection systems that recognize gestures in real time can improve human–computer interaction by making it more intuitive and natural. Colour gloves and skin colour detection are two prominent hand segmentation and detection technologies, but each has its own set of benefits and drawbacks. For physically challenged people, gesture identification is a crucial technique of sharing information. Support vector machine algorithm with principal component analysis, hidden Markov model, superposed network with multiple restricted Boltzmann machines, growing neural gas algorithm, convolutional neural network, double channel convolutional neural network, artificial neural network, and linear support vector machine with gesture dataset matches the interaction of gestures for various postures in real time. Although this method can recognize a huge number of gestures, it does have certain downsides, such as missing movements due to the accuracy of the categorization algorithms. Furthermore, matching the vast dataset takes a longer time. The main contribution of this work lies in a conceptual framework based on the findings of a systematic literature review that provides fruitful implications based on recent research findings and insights that can be used to direct and initiate future research initiatives in the field of hand gesture recognition and artificial intelligence. As a result, a novel study based on a hybrid recurrent neural network (RNN) with chaos game optimization may reduce classification mistakes, increase stability, maximize resilience, and efficiently use the hand gestures recognition system.

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Author information

Authors and Affiliations

  1. P.G. Department of Computer Science & Technology, D.C.P.E, Hanuman Vyayam Prasarak Mandal, Amravati University, Amravati, India

    Jogi John & Shrinivas P. Deshpande

Authors
  1. Jogi John
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  2. Shrinivas P. Deshpande
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Corresponding author

Correspondence to Jogi John .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India

    Prasenjit Chatterjee

  2. Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia

    Dragan Pamucar

  3. Universidad Internacional de Valencia, Valencia, Spain

    Morteza Yazdani

  4. Department of Mechanical Engineering, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India

    Dilbagh Panchal

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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John, J., Deshpande, S.P. (2023). Hand Gesture Identification Using Deep Learning and Artificial Neural Networks: A Review. In: Chatterjee, P., Pamucar, D., Yazdani, M., Panchal, D. (eds) Computational Intelligence for Engineering and Management Applications. Lecture Notes in Electrical Engineering, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-19-8493-8_30

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