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ETLBP and ERDLBP descriptors for efficient facial image retrieval in CBIR systems

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Abstract

The traditional Local Binary Pattern (LBP) employs a 3x3 pixel window and examines the intensity differences between the center pixel and nearby neighbourhood pixels. However, LBP excludes the magnitude of difference information entirely, which highly enhances the discriminative performance between classes. In this work, we propose two new feature descriptors called Extended Transition-LBP (ETLBP) and Extended Radial Difference-LBP (ERDLBP) that include the mean of the magnitude difference of each neighbourhood pixel from the central pixel. The robustness of the proposed descriptors is investigated on four publicly available facial databases. The study has established the effectiveness of the feature descriptors. The experimental findings show that the suggested methods statistically outperformed the existing state-of-the-art methods.

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Data Availability

Four publicly databases are analysed during the current study. These databases include AT &T [5], Yalefaces [43], GT database [12], and faces94 [33]. The link to these databases are available in the reference section.

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Acknowledgements

The authors express their gratitude for the valuable feedback and constructive suggestions provided by the editors and anonymous reviewers, which have been incorporated into the manuscript.

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  1. Indian Institute of Technology, Roorkee, India

    Nitin Arora & Subhash Chander Sharma

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  1. Nitin Arora
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  2. Subhash Chander Sharma
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Correspondence to Nitin Arora.

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Arora, N., Sharma, S.C. ETLBP and ERDLBP descriptors for efficient facial image retrieval in CBIR systems. Multimed Tools Appl 83, 9817–9851 (2024). https://doi.org/10.1007/s11042-023-15832-w

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  • DOI: https://doi.org/10.1007/s11042-023-15832-w

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