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A Novel Hand Gesture Recognition Method Based on Illumination Compensation and Grayscale Adjustment

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Human Centred Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 189))

Abstract

Gesture recognition is a challenging research problem in human–machine systems. Uneven illumination and background noise significantly contribute to this challenge by affecting the accuracy of hand gesture recognition algorithms. To address this challenge, this paper proposes a novel gesture recognition method based on illumination compensation and grayscale adjustment, which can significantly improve gesture recognition in uneven and backlighting conditions. The novelty of the method is in the new illumination compensation algorithm based on luminance adjustment and Gamma correction, which can reduce the luminance value in the overlit image region and enhance the area with low illumination intensity. The grayscale adjustment is used to detect the skin color and hand area accurately. The binary image of the hand gesture is extracted through iterative threshold segmentation, image dilation, and erosion process. Five gesture features including area, roundness, finger peak number, hole number, and average angle are used to recognize the input gesture. The experimental results show that the proposed method can reduce the influence of uneven illumination and effectively recognize the hand gestures. This method can be used in applications involving human–machine interactions conducted in poor lighting conditions.

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Acknowledgements

The authors would like to acknowledge the Centre for Artificial Intelligence, Robotics and Human–Machine Systems (IROHMS) operation C82092, part-funded by the European Regional Development Fund (ERDF) through the Welsh Government. This research is also supported by the National Natural Science Foundation of China (51805280), the Natural Science Foundation of Zhejiang province (LQ18E050005), the Natural Science Foundation of Ningbo (2019A610158), and the Ningbo Technology Innovation 2025 Project (2018B10005).

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Authors and Affiliations

  1. Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China

    Dan Liang & Xiaocheng Wu

  2. Research Centre in AI, Robotics and Human-Machine Systems, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK

    Junshen Chen & Rossitza Setchi

Authors
  1. Dan Liang
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  2. Xiaocheng Wu
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  3. Junshen Chen
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  4. Rossitza Setchi
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Corresponding author

Correspondence to Dan Liang .

Editor information

Editors and Affiliations

  1. Faculty of Informatics, Reutlingen University, Reutlingen, Baden-Württemberg, Germany

    Alfred Zimmermann

  2. KES International and Bournemouth University, Shoreham-by-sea, UK

    Robert J. Howlett

  3. University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Liang, D., Wu, X., Chen, J., Setchi, R. (2021). A Novel Hand Gesture Recognition Method Based on Illumination Compensation and Grayscale Adjustment. In: Zimmermann, A., Howlett, R., Jain, L. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 189. Springer, Singapore. https://doi.org/10.1007/978-981-15-5784-2_10

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