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Dual neighborhood thresholding patterns based on directional sampling

Effective face representation on mixed face recognition dataset

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Abstract

The evaluation of face recognition algorithms relies on the diversity of the challenges simulated on the adopted benchmarks. The main face recognition challenges cover the illumination changes, inhomogeneous background, different facial expressions, pose variations, occlusion, aging and resolution. Many 2D databases that include one challenge or more have been proposed in the state of the art. These databases represent different amount of individuals and samples, and generally the number of persons does not exceed 100 classes. The reason behind this limitation relies on the resources and materials required to construct a database composed of thousands of images and hundreds of persons along with the mentioned face recognition basic challenges. As a solution, researchers proposed to build benchmarks based on collecting the web images of celebrities from search engines such as Google Images and Flicker. The well-known database of this kind is Labeled Faces in the Wild (LFW) as a public benchmark for face verification. This solution managed to constitute a dependent way to construct benchmarks, but it could not be applicable for face recognition since the collected images have a low resolution and the majority of the persons are represented over few samples (one or two in most cases), which made these databases extremely hard for handcrafted-based face recognition systems. In this paper, we propose to construct a challenging database referred to as mixed face recognition database (MFRD) based on gathering the images of eight well-known benchmarks of the literature (FERET, Extended Yale B, ORL, AR, FEI, KDEF, IMM and JAFFE). The constructed database is expected to be more complex in terms of the amount of classes/images and the diversity of challenges. We expect then that the recognition performance on this database will drop compared to the one recorded on each considered benchmark individually. This paper presents also a new LBP variant, namely dual neighborhood thresholding patterns based on directional sampling (DNTPDS) as a robust and computationally efficient handcrafted descriptor for face recognition. The concept behind this new descriptor is based on defining a \(5\times 5\) neighborhood topology, that relies on a directional sampling to select the only 16 prominent neighbors instead of 25. The proposed DNTPDS operator demonstrates a superior performance and outperforms 18 state-of-the-art LBP variants that is proved through a set of comprehensive experiments.

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Acknowledgements

The authors gratefully acknowledge the funding received from CNSRT-Maroc (Centre National de la Recherche Scientifique et Technique) and the French government (Eiffel scholarship).

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Authors and Affiliations

  1. Laboratoire LASTID, Département de Physique, Faculté des Sciences, Université Ibn Tofail, BP 133, 14000, Kenitra, Maroc

    M. Kas, Y. El-merabet & R. Messoussi

  2. CIAD UMR 7533, University Bourgogne Franche-Comté, UTBM, 90010, Belfort, France

    M. Kas & Y. Ruichek

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  1. M. Kas
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  2. Y. El-merabet
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  3. Y. Ruichek
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  4. R. Messoussi
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Correspondence to M. Kas.

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Kas, M., El-merabet, Y., Ruichek, Y. et al. Dual neighborhood thresholding patterns based on directional sampling. Knowl Inf Syst 65, 435–462 (2023). https://doi.org/10.1007/s10115-022-01720-6

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  • DOI: https://doi.org/10.1007/s10115-022-01720-6

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