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Multiple Face Detection Using Hybrid Features with SVM Classifier

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Data and Communication Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 847))

Abstract

Nowadays, multiple face detection (MFD) and extraction play an important role in face identification for various applications. In the proposed algorithm, Support Vector Machine (SVM) has been used for multiple face detection, and Discrete Wavelet Transform (DWT), Edge Histogram (EH), and Auto-correlogram (AC) are used for feature extraction. The proposed methodology worked on two different database i.e. Carnegie Mellon University (CMU) and BAO database for MFD. In this research paper, the proposed methodology gives a better result than the existing technique. Finally, our accuracy raised up to 90% approximately.

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Author information

Authors and Affiliations

  1. Department of Electronics & Communication, PEC University of Technology, Chandigarh, India

    Sandeep Kumar & Sukhwinder Singh

  2. Department of Electrical Engineering, PEC University of Technology, Chandigarh, India

    Jagdish Kumar

Authors
  1. Sandeep Kumar
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  2. Sukhwinder Singh
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  3. Jagdish Kumar
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Corresponding author

Correspondence to Sandeep Kumar .

Editor information

Editors and Affiliations

  1. University of Canberra, Adelaide, SA, Australia

    Lakhmi C. Jain

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Prashant Johri

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Kumar, S., Singh, S., Kumar, J. (2019). Multiple Face Detection Using Hybrid Features with SVM Classifier. In: Jain, L., E. Balas, V., Johri, P. (eds) Data and Communication Networks. Advances in Intelligent Systems and Computing, vol 847. Springer, Singapore. https://doi.org/10.1007/978-981-13-2254-9_23

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  • DOI: https://doi.org/10.1007/978-981-13-2254-9_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2253-2

  • Online ISBN: 978-981-13-2254-9

  • eBook Packages: EngineeringEngineering (R0)

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