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Gabor Filter meanPCA Feature Extraction for Gender Recognition

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Proceedings of 2nd International Conference on Computer Vision & Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 704))

Abstract

This paper proposed Novel feature extraction techniques as Gabor-meanPCA for automatic gender recognition using faces of person. Feature extraction is the main stage on which accuracy of gender recognition system depended. Male and female have different edge and texture pattern on faces. Gabor filter is able to extract edges and texture pattern of faces but has a problem of huge dimension and high redundancy. In this paper, Gabor filter is used for extraction of edge pattern of faces using different angles. Problem of huge dimension and high redundancy is reduced by proposed two-level feature reduction technique. The proposed technique also provides better accuracy as well as compact feature vector for reducing classification time.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Malaviya National Institute of Technology, Jaipur, 302017, Rajasthan, India

    Sandeep K. Gupta & Neeta Nain

Authors
  1. Sandeep K. Gupta
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  2. Neeta Nain
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Corresponding author

Correspondence to Sandeep K. Gupta .

Editor information

Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Mohan S. Kankanhalli

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Gupta, S.K., Nain, N. (2018). Gabor Filter meanPCA Feature Extraction for Gender Recognition. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_7

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  • DOI: https://doi.org/10.1007/978-981-10-7898-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7897-2

  • Online ISBN: 978-981-10-7898-9

  • eBook Packages: EngineeringEngineering (R0)

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