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Zernike Moments and Machine Learning Based Gender Classification Using Facial Images

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Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016) (SoCPaR 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 614))

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Abstract

This paper proposes a zernike moments based gender classification system using facial image. Gender classification is achieved by training the Bayesian, Support vector machine, Linear discriminant analysis and Neural network classifiers. The proposed method was evaluated using ORL and faces94 databases. The simulation results indicate the effectiveness of the method in achieving the greater accuracy even under the variations in pose, scale, rotation and occlusion. In particular the neural network classifier was excellent in providing classification accuracy of 95% and 100% respectively with 25 zernike moments for ORL and faces94 database.

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Author information

Authors and Affiliations

  1. School of Electronics Engineering, VIT University, Vellore, 632014, Tamil Nadu, India

    Vijayalakshmi G. V. Mahesh

  2. Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, China

    Alex Noel Joseph Raj

Authors
  1. Vijayalakshmi G. V. Mahesh
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  2. Alex Noel Joseph Raj
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Corresponding author

Correspondence to Vijayalakshmi G. V. Mahesh .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

  2. VIT University, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. School of Engineering, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Ana Maria Madureira

  4. Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Mahesh, V.G.V., Raj, A.N.J. (2018). Zernike Moments and Machine Learning Based Gender Classification Using Facial Images. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_39

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  • DOI: https://doi.org/10.1007/978-3-319-60618-7_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60617-0

  • Online ISBN: 978-3-319-60618-7

  • eBook Packages: EngineeringEngineering (R0)

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