Skip to main content

Advertisement

Springer Nature Link
Log in

Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Face recognition applications focus on local features to prevent detailed information from being omitted while the feature extraction processes. This paper is based on presenting a local pattern-based model to extract more discriminative features that lead to more accurate classification. In local pattern-based feature extraction, the LBP is one of the most important approaches that many variants of this method have been proposed till now. LBP calculation is based on differences between the central pixel and the desired one. In contrast, the information hidden in the selected pixel’s neighborhood pixels is not included in this process. This paper proposes the DR_LBP approach to address this failure by defining distances and using some of them in a ratio form. Successful results have been earned in many experimental results. In LBP, the calculations’ primary flow takes advantage of two pixels in the LBP box, the central and the desired pixel. Contrary to the original LBP, this paper’s proposed approach uses three pixels of LBP box to conduct the feature vector, which leads to employing the information hidden in the relationship between neighboring pixels. This approach applies the experiments on two standard datasets, ORL Yale face and Faces94 dataset. The accuracy percent of the proposed plan is 95.95, 94.09 and 98.01 on ORL, Yale face and Faces94 dataset, respectively, which is the reason to present this model as a new face feature extraction approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Computer vision using local binary patterns, vol 40. Springer, Berlin

    Book  Google Scholar 

  2. Delac K, Grgic M, Grgic S (2008) Recent advances in face recognition. BoD–Books on Demand, Norderstedt

    Book  Google Scholar 

  3. Zhou L, Li W, Du Y, Lei B, Liang S (2019) Adaptive illumination-invariant face recognition via local nonlinear multi-layer contrast feature. J Vis Commun Image Represent 64:102641

    Article  Google Scholar 

  4. Zheng W, Gou C, Wang F-Y (2020) A novel approach inspired by optic nerve characteristics for few-shot occluded face recognition. Neurocomputing 376:25–41

    Article  Google Scholar 

  5. Hussain SU, Napoléon T, Jurie F (2012) Face recognition using local quantized patterns

  6. Yang M, Zhang L, Shiu SC-K, Zhang D (2012) Monogenic binary coding: an efficient local feature extraction approach to face recognition. IEEE Trans Inf Forensics Secur 7(6):1738–1751

    Article  Google Scholar 

  7. Yan K, Chen Y, Zhang D (2011) Gabor surface feature for face recognition. In: The first asian conference on pattern recognition, 2011, pp 288–292

  8. Vu N-S (2012) Exploring patterns of gradient orientations and magnitudes for face recognition. IEEE Trans Inf Forensics Secur 8(2):295–304

    Article  Google Scholar 

  9. Nguyen H-T, Caplier A (2015) Local patterns of gradients for face recognition. IEEE Trans Inf Forensics Secur 10(8):1739–1751

    Article  Google Scholar 

  10. Brahnam S, Jain LC, Nanni L, Lumini A (2014) Local binary patterns: new variants and applications, vol 2. Springer, Berlin

    Book  Google Scholar 

  11. Yang W, Zhang X, Li J (2020) A local multiple patterns feature descriptor for face recognition. Neurocomputing 373:109–122

    Article  Google Scholar 

  12. Hadizadeh H (2015) Noise-resistant and rotation-invariant texture description and representation using local Gabor wavelets binary patterns. In: 2015 The international symposium on artificial intelligence and signal processing (AISP), 2015, pp 30–34

  13. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  14. He X, Yan S, Hu Y, Niyogi P, Zhang H-J (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  15. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  16. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  17. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  18. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  19. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), 2005, vol. 1, pp 886–893

  20. Tuceryan M, Jain AK (1998) The handbook of pattern recognition and computer vision, chapter 2.1. In: Texture analysis. World Scientific Co., Singapore pp 207–248

  21. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Article  Google Scholar 

  22. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473

    Article  Google Scholar 

  23. Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 1:25–39

    Article  Google Scholar 

  24. Thyagarajan KS, Nguyen T, Persons CE (1994) A maximum likelihood approach to texture classification using wavelet transform. In: Proceedings of 1st international conference on image processing, 1994, vol 2, pp 640–644

  25. Nguyen HT, Caplier A (2012) Elliptical local binary patterns for face recognition. In: Asian conference on computer vision, 2012, pp 85–96

  26. 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  Google Scholar 

  27. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing, 2008, pp 236–243

  28. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  Google Scholar 

  29. Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. In: International conference on biometrics, 2007, pp 828–837

  30. Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved LBP under Bayesian framework. In: Third international conference on image and graphics (ICIG’04), 2004, pp 306–309

  31. Vu NS, Caplier A (2010) Face recognition with patterns of oriented edge magnitudes. In: European conference on computer vision, 2010, pp 313–326

  32. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Tenth IEEE international conference on computer vision (ICCV’05) Volume 1, 2005, vol. 1, pp 786–791

  33. Liao S, Chung ACS (2009) Face recognition with salient local gradient orientation binary patterns. In: 2009 16th IEEE international conference on image processing (ICIP), 2009, pp 3317–3320

  34. Shan S, Zhang W, Su Y, Chen X, Gao W (2006) Ensemble of piecewise FDA based on spatial histograms of local (Gabor) binary patterns for face recognition. In: 18th International conference on pattern recognition (ICPR’06), 2006, vol. 4, pp 606–609

  35. Ruichek Y (2018) Local concave-and-convex micro-structure patterns for texture classification. Pattern Recognit 76:303–322

    Article  Google Scholar 

  36. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188

    Article  Google Scholar 

  37. Wang T, Zhu L (2013) Sparse sufficient dimension reduction using optimal scoring. Comput Stat Data Anal 57(1):223–232

    Article  MathSciNet  Google Scholar 

  38. Zhu Q, Yuan N, Guan D, Xu N, Li H (2019) An alternative to face image representation and classification. Int J Mach Learn Cybern 10(7):1581–1589

    Article  Google Scholar 

  39. Yee SY, Rassem TH, Mohammed MF, Awang S (2020) Face recognition using Laplacian completed local ternary pattern (LapCLTP). In: Advances in electronics engineering, Springer, 2020, pp 315–327

  40. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  Google Scholar 

  41. Zhang Q, Li B (2010) Discriminative K-SVD for dictionary learning in face recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition, 2010, pp 2691–2698

  42. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  43. Luo X, Xu Y, Yang J (2019) Multi-resolution dictionary learning for face recognition. Pattern Recognit. 93:283–292

    Article  Google Scholar 

  44. Truong HP, Kim YG (2018) Enhanced line local binary patterns (EL-LBP): an efficient image representation for face recognition. In: International conference on advanced concepts for intelligent vision systems, 2018, pp 285–296

  45. Reddy KS, Kumar VV, Reddy BE (2015) Face recognition based on texture features using local ternary patterns. Int J Image Graph Signal Process. 7(10):37

    Google Scholar 

  46. Hond D, Spacek L (1997) Distinctive descriptions for face processing. In BMVC, no 0.2, pp 0–4

  47. Vinay A, Hebbar D, Shekhar VS, Murthy KNB, Natarajan S (2015) Two novel detector-descriptor based approaches for face recognition using sift and surf. Procedia Comput Sci 70:185–197

    Article  Google Scholar 

  48. Gupta S, Thakur K, Kumar M (2020) 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. Vis Comput. https://doi.org/10.1007/s00371-020-01814-8

    Article  Google Scholar 

  49. Hamdan B, Mokhtar K (2018) Face recognition using angular radial transform. J King Saud Univ Inf Sci 30(2):141–151

    Google Scholar 

  50. He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, 2004, pp 153–160

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Saeed Najafi Khanbebin & Vahid Mehrdad

Authors
  1. Saeed Najafi Khanbebin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vahid Mehrdad
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vahid Mehrdad.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Najafi Khanbebin, S., Mehrdad, V. Local improvement approach and linear discriminant analysis-based local binary pattern for face recognition. Neural Comput & Applic 33, 7691–7707 (2021). https://doi.org/10.1007/s00521-020-05512-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05512-3

Keywords