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Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations

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Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

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Abstract

Among numerous local descriptors persisting in literature, LPQ is one of most influential blur invariant descriptor. But when high content blurring is mixed with light variations (moderate and high) then LPQ and many others fails to achieve required robustness to declare the efficient face descriptor. This gap is filled by introducing advance versions of LPQ so-called Enhanced LPQ1 (ELPQ1) and ELPQ2. Precisely, blurred affected image (with light variations), by using Gaussian low pass filtering, is passed to 2 novel pre-processing approaches before LPQ feature extraction. In former approach edge oriented (enhanced) images are produced by 3 methods i.e. Sobel (magnitude gradient), Image sharpening and Kirsch (magnitude gradient). In latter approach, Sobel (magnitude gradient) is replaced by more robust magnitude gradient, called Sobel + Prewitt (magnitude gradient), produced by using Sobel horizontal gradient and Prewitt vertical gradient. Rest 2 methods remains similar in both the pre-processing approaches. Further LPQ is imposed for feature extraction. As each pre-processing approach contains 3 methods therefore 3 transformed images are produced after LPQ. Histograms under respective category are integrated to build feature size of 2 robust blur invariant representations ELPQ1 and ELPQ2. Compressed feature size is attained from FLDA and classification is assisted from SVMs. Experiments on GT and EYB proves efficacy of ELPQ1 and ELPQ2 against benchmark methods.

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Authors and Affiliations

  1. CSE Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Shekhar Karanwal & Manoj Diwakar

Authors
  1. Shekhar Karanwal
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  2. Manoj Diwakar
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Correspondence to Shekhar Karanwal .

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Editors and Affiliations

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

    Ajith Abraham

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Matieland, South Africa

    Andries Engelbrecht

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Fabio Scotti

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Mumbai, Mumbai, Maharashtra, India

    Pooja Manghirmalani Mishra

  6. University of Calabria (Unical), Rende, Italy

    Giancarlo Fortino

  7. Department of Informatics, Vilnius University, Kaunas, Lithuania

    Virgilijus Sakalauskas

  8. Center for Smart Computing Continuum, Forschung Burgenland, Eisenstadt, Austria

    Sabri Pllana

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Karanwal, S., Diwakar, M. (2022). Enhanced LPQ Based Two Novel Blur Invariant Face Descriptors in Light Variations. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_14

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