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An Effective Pipeline for Depth Image-Based Hand Gesture Recognition

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Computational Intelligence in Pattern Recognition (CIPR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 725))

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Abstract

In this paper, a pipeline for hand gesture recognition from depth images is presented. This depth-based image recognition system is capable of recognizing gestures with challenges like varying depths, complex backgrounds, and variation in view point, hand pose, and appearance. Firstly, we obtain a grayscale image from the depth map, segment the hand region, and perform orientation normalization and feature extraction, which is followed by classification. Two different sets of feature descriptors are extracted: Multi-Radii Circular Signatures (MRCS) and Multi-Scale Density (MSD). Different classifiers have been used to demonstrate the efficacy of the suggested pipeline. Overall accuracy of 98.90% (MRCS) and 99.78% (MSD) is obtained using the MLP classifier.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dumkal Institute of Engineering and Technology, Basantapur, Murshidabad, 742406, India

    Taniya Sahana

  2. Department of Computer Science and Engineering, Aliah University, IIA/27 Newtown, Kolkata, 700160, India

    Taniya Sahana & Ayatullah Faruk Mollah

Authors
  1. Taniya Sahana
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  2. Ayatullah Faruk Mollah
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Corresponding author

Correspondence to Taniya Sahana .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University (MSCB), Baripada, Odisha, India

    Janmenjoy Nayak

  3. Department of Computer Applications, Veer Surendra Sai University of Technology, Burla, Odisha, India

    Bighnaraj Naik

  4. Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

    S. Vimal

  5. Department of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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

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Sahana, T., Mollah, A.F. (2023). An Effective Pipeline for Depth Image-Based Hand Gesture Recognition. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_40

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