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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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References

  1. Abhishree T, Latha J, Manikantan K, Ramachandran S (2015) Face recognition using gabor filter based feature extraction with anisotropic diffusion as a pre-processing technique. Procedia Comput Sci 45:312–321

    Article  Google Scholar 

  2. Akhtar Z, Rattani A (2017) A face in any form: new challenges and opportunities for face recognition technology. Computer 50(4):80–90. https://doi.org/10.1109/MC.2017.119

    Article  Google Scholar 

  3. Bereta M, Pedrycz W, Reformat M (2013) Local descriptors and similarity measures for frontal face recognition: a comparative analysis. J Vis Commun Image Represent 24(8):1213–1231

    Article  Google Scholar 

  4. Bhattacharjee D, Roy H (2021) Pattern of local gravitational force (PLGF): a novel local image descriptor. IEEE Trans Pattern Anal Mach Intell 43(2):595–607. https://doi.org/10.1109/TPAMI.2019.2930192

    Article  Google Scholar 

  5. Cavalcanti GD, Ren TI, Pereira JF (2013) Weighted modular image principal component analysis for face recognition. Expert Syst Appl 40(12):4971–4977

    Article  Google Scholar 

  6. Ding C, Choi J, Tao D, Davis LS (2016) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell 38(3):518–531. https://doi.org/10.1109/TPAMI.2015.2462338

    Article  Google Scholar 

  7. Ding C, Choi J, Tao D, Davis LS (2016) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell 38(3):518–531

    Article  Google Scholar 

  8. Dubey SR (2017) Local directional relation pattern for unconstrained and robust face retrieval. arXiv preprint arXiv:1709.09518

  9. El Aroussi M, El Hassouni M, Ghouzali S, Rziza M, Aboutajdine D (2011) Local appearance based face recognition method using block based steerable pyramid transform. Signal Process 91(1):38–50

    Article  MATH  Google Scholar 

  10. El Khadiri I, Kas M, El Merabet Y, Ruichek Y, Touahni R (2018) Repulsive-and-attractive local binary gradient contours: new and efficient feature descriptors for texture classification. Inf Sci 467:634–653

  11. Elmerabet Y et al (2019) Attractive-and-repulsive center-symmetric local binary patterns for texture classification. Eng Appl Artif Intell 78:158–172

    Article  Google Scholar 

  12. Fathi A, Alirezazadeh P, Abdali-Mohammadi F (2016) A new Global-Gabor-Zernike feature descriptor and its application to face recognition. J Vis Commun Image Represent 38:65–72

    Article  Google Scholar 

  13. Guan N, Tao D, Luo Z, Yuan B (2012) Nenmf: an optimal gradient method for nonnegative matrix factorization. IEEE Trans Signal Process 60(6):2882–2898

    Article  MathSciNet  MATH  Google Scholar 

  14. Heisele B, Serre T, Poggio T (2007) A component-based framework for face detection and identification. Int J Comput Vis 74(2):167–181

    Article  Google Scholar 

  15. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07-49, University of Massachusetts, Amherst

  16. Huang ZH, Li WJ, Shang J, Wang J, Zhang T (2015) Non-uniform patch based face recognition via 2d-dwt. Image Vis Comput 37:12–19

    Article  Google Scholar 

  17. ul Hussain S, Triggs B (2012) Visual recognition using local quantized patterns. In: European conference on computer vision. Springer, pp 716–729

  18. Karanwal S, Diwakar M (2021) Od-lbp: Orthogonal difference-local binary pattern for face recognition. Digital Signal Process 110:102948. https://doi.org/10.1016/j.dsp.2020.102948. https://www.sciencedirect.com/science/article/pii/S1051200420302931

  19. Kas M, Ruichek Y, Messoussi R et al (2018) Mixed neighborhood topology cross decoded patterns for image-based face recognition. Expert Syst Appl 114:119–142

    Article  Google Scholar 

  20. Kaya Y, Ertuğrul ÖF, Tekin R (2015) Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 34:728–735

    Article  Google Scholar 

  21. Khadiri IE, Chahi A, merabet YE, Ruichek Y, Touahni R (2018) Local directional ternary pattern: A new texture descriptor for texture classification. Comput Vis Image Underst 169:14–27

    Article  Google Scholar 

  22. Lu GF, Zou J, Wang Y (2012) Incremental complete LDA for face recognition. Pattern Recogn 45(7):2510–2521

    Article  MATH  Google Scholar 

  23. Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings 3rd IEEE international conference on automatic face and gesture recognition. IEEE, pp 200–205

  24. Mehta R, Egiazarian K (2016) Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recogn Lett 71:16–22

    Article  Google Scholar 

  25. Ouslimani F, Ouslimani A, Ameur Z (2019) Rotation-invariant features based on directional coding for texture classification. Neural Comput Appl 31(10):6393–6400

    Article  Google Scholar 

  26. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The feret evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104

    Article  Google Scholar 

  27. Secchi P, Vantini S, Zanini P (2016) Hierarchical independent component analysis: a multi-resolution non-orthogonal data-driven basis. Comput Stat Data Anal 95:133–149

    Article  MathSciNet  MATH  Google Scholar 

  28. Song K, Yan Y, Zhao Y, Liu C (2015) Adjacent evaluation of local binary pattern for texture classification. J Vis Commun Image Represent 33:323–339

    Article  Google Scholar 

  29. Song T, Xin L, Gao C, Zhang T, Huang Y (2021) Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation. Pattern Recogn 115:107891. https://doi.org/10.1016/j.patcog.2021.107891. https://www.sciencedirect.com/science/article/pii/S0031320321000789

  30. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    Article  MathSciNet  MATH  Google Scholar 

  31. Topi M, Timo O, Matti P, Maricor S (2000) Robust texture classification by subsets of local binary patterns. In: Pattern recognition, 2000. Proceedings. 15th international conference on, vol 3. IEEE, pp 935–938

  32. Verma M, Raman B (2018) Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval. Multimedia Tools Appl 77(10):11843–11866

    Article  Google Scholar 

  33. Vipparthi SK, Murala S, Nagar SK, Gonde AB (2015) Local gabor maximum edge position octal patterns for image retrieval. Neurocomputing 167:336–345

    Article  Google Scholar 

  34. Vipparthi SK, Nagar SK (2016) Local extreme complete trio pattern for multimedia image retrieval system. Int J Autom Comput 13(5):457–467

    Article  Google Scholar 

  35. Yang W, Wang Z, Zhang B (2016) Face recognition using adaptive local ternary patterns method. Neurocomputing 213:183–190

    Article  Google Scholar 

  36. Zeng H, Chen J, Cui X, Cai C, Ma KK (2016) Quad binary pattern and its application in mean-shift tracking. Neurocomputing 217:3–10

    Article  Google Scholar 

Download references

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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