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Human Gender Classification Based on Hand Images Using Deep Learning

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Artificial Intelligence (ISAI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1695))

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Abstract

Soft biometric traits (e.g., gender, age, etc. can characterize very relevant personal information. The hand-based traits are studied for traditional/hard biometric recognition for diverse applications. However, little attention is focused to tackle soft biometrics using hand images. In this paper, human gender classification is addressed using the frontal and dorsal hand images of a human. A new hand dataset is created at the Jadavpur University, India denoted as JU-HD for experiments. It represents significant posture variations in an uncontrolled laboratory environment. Sample hand images of 57 persons are collected to incorporate more user-flexibility in posing the hands that incur additional challenges to discriminate the person’s gender. Five backbone CNNs are used to develop a deep model for gender classification. The method achieves 90.49% accuracy on JU-HD using Inception-v3. Also, improved gender classification accuracy is achieved on 11k hands dataset (Easy-Chair Pre-print: [27].).

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Acknowledgement

Authors are thankful to the Center for Microprocessor Applications for Training Education and Research (CMATER) Lab, Computer Science and Engineering Department, Jadavpur University, Kolkata-32, India for providing infrastructural resources, and Scholars for dataset preparation and progress during this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, WB, India

    Rajesh Mukherjee

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, 333 031, Rajasthan, India

    Asish Bera

  3. Department of Computer Science and Engingeering, Jadavpur University, Kolkata, WB, India

    Debotosh Bhattacharjee & Mita Nasipuri

Authors
  1. Rajesh Mukherjee
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  2. Asish Bera
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  3. Debotosh Bhattacharjee
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  4. Mita Nasipuri
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Corresponding author

Correspondence to Asish Bera .

Editor information

Editors and Affiliations

  1. XIM University, Bhubaneswar, India

    Arif Ahmed Sk

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Turki Turki

  3. Haldia Institute of Technology, Haldia, India

    Tarun Kumar Ghosh

  4. Haldia Institute of Technology, Haldia, India

    Subhankar Joardar

  5. Haldia Institute of Technology, Haldia, India

    Subhabrata Barman

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Mukherjee, R., Bera, A., Bhattacharjee, D., Nasipuri, M. (2022). Human Gender Classification Based on Hand Images Using Deep Learning. In: Sk, A.A., Turki, T., Ghosh, T.K., Joardar, S., Barman, S. (eds) Artificial Intelligence. ISAI 2022. Communications in Computer and Information Science, vol 1695. Springer, Cham. https://doi.org/10.1007/978-3-031-22485-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-22485-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22484-3

  • Online ISBN: 978-3-031-22485-0

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