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Advanced Computing and Intelligent Technologies pp 99–116Cite as

A Comprehensive Comparative Study Between LBP and LBP Variants in Face Recognition

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)

Abstract

LBP is renowned as one of the most powerful local descriptors for texture description. The merits of LBP are monotonic gray invariance property and less complex algorithm. Therefore, LBP was deployed successfully in diverse range of applications. The success of LBP has inspired researchers to develop new LBP variants for diverse range of applications. These LBP variants achieve good results with respect to the application they were developed. After observing merits of LBP and its variants, it is found that there is need for comprehensive comparative study among these LBP-based descriptors and chose best among all descriptors. With this note, the proposed work provides comprehensive comparative study between 15 LBP-based descriptors which includes LBP and 14 LBP variants. Apart from LBP, the other 14 are HELBP, VELBP, NI-LBP, AD-LBP, DLBP, tLBP, RD-LBP, MRELBP-NI, MB-ZZLBP, MBP,6 × 6 MB-LBP, OC-LBP, LDBP and LNDBP. For all descriptors, the features are extracted globally, and then, PCA is used for feature compaction. Ultimately classification is performed by RBF, the SVMs-based method. Experiments performed on ORL face dataset confirm that among all 15, it is MB-ZZLBP which secures superior accuracy than other 14 descriptors. MB-ZZLBP also out classes numerous methods from literature.

Keywords

  • Feature extraction
  • Local descriptors
  • Global descriptors
  • Classifier
  • Grayscale images

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Authors and Affiliations

  1. Department of CSE, Graphic Era University (Deemed), Dehradun, Uttarakhand, 248002, India

    Shekhar Karanwal

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  1. Shekhar Karanwal
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Correspondence to Shekhar Karanwal .

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Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Rabindra Nath Shaw

  2. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

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Karanwal, S. (2022). A Comprehensive Comparative Study Between LBP and LBP Variants in Face Recognition. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_9

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  • DOI: https://doi.org/10.1007/978-981-19-2980-9_9

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