Skip to main content
Log in

Comparative study of 1D-local descriptors for ear biometric system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As an important modality for human identification, Ear based biometric has achieved a relatively mature level of development, as it faces higher challenges surrounded by the real-world applications of biometric technology. One such challenge is extracting a unique template that leads to making a reliable identification task. In the most existing ear biometric approaches, the features were calculated from the 2D and 3D images. In the presented work, we have well investigated the performance of 1D-LBP and its variations (i.e., standard 1D-LBP, shifted-1D-LBP, 1D-Multi-Resolution-LBP, Local Centroid Pattern, Local Ternary Pattern, Local neighbor gradient pattern and 1D-Noise-tolerant local binary pattern) on ear recognition. Typically, the 1D-LBP treats the ear image as a 1D vector where the histograms of the produced image are then used as features to describe a human ear. The experimental results show that the LBP’s in 1D is promising in developing a robust handcrafted feature for ear recognition.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Alagarsamy SB, Murugan K (2020) Ear recognition system using adaptive approach runge-kutta (aark) threshold segmentation with cart classifier. Multimed Tools Appl 79(15):10445–10459

    Article  Google Scholar 

  2. Annapurani K, Sadiq M, Malathy C (2015) Fusion of shape of the ear and tragus–a unique feature extraction method for ear authentication system. Expert Syst Appl 42(1):649–656

    Article  Google Scholar 

  3. Anwar AS, Ghany KKA, Elmahdy H (2015) Human ear recognition using geometrical features extraction. Procedia Comput Sci 65:529–537

    Article  Google Scholar 

  4. Benzaoui A, Adjabi I, Boukrouche A (2017) Experiments and improvements of ear recognition based on local texture descriptors. Opt Eng 56(4):043109

    Article  Google Scholar 

  5. Benzaoui A, Hadid A, Boukrouche A (2014) Ear biometric recognition using local texture descriptors. J Electron Imag 23(5):053008

    Article  Google Scholar 

  6. Bertillon A (1890) La photographie judiciaire: avec un appendice sur la classification et l’identification anthropométriques. Gauthier-Villars, Paris

    Google Scholar 

  7. Boodoo NB, Jahangeer, Baichoo S (2013) Lbp-based ear recognition. In: 13th IEEE international conference on bioinformatics and bioengineering, pp 1–4. IEEE

  8. Chatlani N, Soraghan JJ (2010) Local binary patterns for 1-d signal processing. In: Signal processing conference, 2010 18th European, pp 95–99. IEEE

  9. Doghmane H, Boukrouche A, Boubchir L (2019) A novel discriminant multiscale representation for ear recognition. Int J Biometr 11(1):50–66

    Article  Google Scholar 

  10. Emeršič ž, Štepec D, Štruc V, Peer P, George A, Ahmad A, Omar E, Boult TE, Safdaii R, Zhou Y et al (2017) The unconstrained ear recognition challenge. In: 2017 IEEE international joint conference on biometrics (IJCB), pp 715–724. IEEE

  11. Emeršič ž, Štruc V, Peer P (2017) Ear recognition: more than a survey. Neurocomputing 255:26–39

    Article  Google Scholar 

  12. Ertuǧrul ÖF, Kaya Y, Tekin R, Almalı MN (2016) Detection of parkinson’s disease by shifted one dimensional local binary patterns from gait. Expert Syst Appl 56:156–163

    Article  Google Scholar 

  13. Fadaei S, Amirfattahi R, Ahmadzadeh MR (2017) Local derivative radial patterns: a new texture descriptor for content-based image retrieval. Signal Process 137:274–286

    Article  Google Scholar 

  14. Ghoualmi L, Draa A, Chikhi S (2015) Ear feature extraction using a dwt-sift hybrid. In: Intelligent data analysis and applications, pp 37–47. Springer

  15. Gonzalez E, Alvarez L, Mazorra L (2008) Ami ear database. Centro de I+ D de Tecnologias de la Imagen

  16. Hassaballah M, Alshazly HA, Ali AA (2019) Ear recognition using local binary patterns: a comparative experimental study. Expert Syst Appl 118:182–200

    Article  Google Scholar 

  17. Hassaballah M, Alshazly HA, Ali AA (2020) Robust local oriented patterns for ear recognition. Multimed Tools Appl 79(41):31183–31204

    Article  Google Scholar 

  18. Hongwei H, Peng G, Wang X, Zhou Z (2018) Weld defect classification using 1-d lbp feature extraction of ultrasonic signals. Nondestruct Test Eval 33 (1):92–108

    Article  Google Scholar 

  19. Houam L, Hafiane A, Jennane R, Boukrouche A, Lespessailles E (2010) Trabecular bone anisotropy characterization using 1d local binary patterns. In: International conference on advanced concepts for intelligent vision systems, pp 105–113. Springer

  20. Huang X, Wang S-J, Zhao G, Piteikainen M (2015) Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection. In: Proceedings of the IEEE international conference on computer vision workshops, pp 1–9

  21. Iannerelli A (1989) Ear identification forensic identification series

  22. Jahangeer NB, Boodoo, Subramanian RK (2009) Robust multi biometric recognition using face and ear images. arXiv:0912.0955

  23. Jain AK, Flynn P, Ross AA (2007) Handbook of biometrics. Springer Science & Business Media

  24. Jaiswal AK, Banka H (2018) Local transformed features for epileptic seizure detection in eeg signal. J Med Biol Eng 38(2):222–235

    Article  Google Scholar 

  25. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization

  26. Kaya Y, Ertuǧrul ÖF (2018) A stable feature extraction method in classification epileptic eeg signals. Australasian Phys Eng Sci Med 41(3):721–730

    Article  Google Scholar 

  27. Kaya Y, Uyar M, Tekin R, Yıldırım S (2014) 1d-local binary pattern based feature extraction for classification of epileptic eeg signals. Appl Math Comput 243:209–219

    MathSciNet  MATH  Google Scholar 

  28. Kylberg G, Sintorn I-M (2013) Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Vid Process 2013(1):17

    Article  Google Scholar 

  29. Liu Y, Zhang B, Lu G, Zhang D (2016) Online 3d ear recognition by combining global and local features. PloS one 11(12):e0166204

    Article  Google Scholar 

  30. Louis W, Hatzinakos D, Venetsanopoulos A (2014) One dimensional multi-resolution local binary patterns features (1dmrlbp) for regular electrocardiogram (ecg) waveform detection. In: 2014 19th International conference on digital signal processing (DSP), pp 601–606. IEEE

  31. Lu S, Wang S-H, Zhang Y-D (2020) Detection of abnormal brain in mri via improved alexnet and elm optimized by chaotic bat algorithm. Neural Comput Applic, 1–13

  32. Mehraj H, Mir AH (2020) Human recognition using ear based deep learning features. In: 2020 International conference on emerging smart computing and informatics (ESCI), pp 357–360. IEEE

  33. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  34. Pflug A, Busch C (2012) Ear biometrics: a survey of detection, feature extraction and recognition methods. IET Biometr 1(2):114–129

    Article  Google Scholar 

  35. Pietikäinen M, Hadid A, Zhao G, Ahonen T (2011) Computer vision using local binary patterns, vol 40. Springer Science & Business Media

  36. Radhika K, Devika K, Aswathi T, Sreevidya P, Sowmya V, Soman KP (2020) Performance analysis of nasnet on unconstrained ear recognition. In: Nature inspired computing for data science, pp 57–82. Springer

  37. Regouid M, Benouis M (2018) Shifted 1d-lbp based ecg recognition system. In: International symposium on modelling and implementation of complex systems, pp 168–179. Springer

  38. Regouid M, Touahria M, Benouis M, Costen N (2019) Multimodal biometric system for ecg, ear and iris recognition based on local descriptors. Multimed Tools Appl, 1–27

  39. Sairamya NJ, Thomas George S, Balakrishnan R, Subathra MSP (2018) An effective approach to classify epileptic eeg signal using local neighbor gradient pattern transformation methods. Australasian Phys Eng Sci Med 41(4):1029–1046

    Article  Google Scholar 

  40. Sayed MT, Chalechale A (2019) Local binary patterns for noise-tolerant semg classification. SIViP 13(3):491–498

    Article  Google Scholar 

  41. Sepas-Moghaddam A, Pereira F, Correia PL (2018) Ear recognition in a light field imaging framework: a new perspective. IET Biometrics 7(3):224–231

    Article  Google Scholar 

  42. Tan X, Bill T (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 

  43. Tirunagari S, Kouchaki S, Abasolo D, Poh N (2017) One dimensional local binary patterns of electroencephalogram signals for detecting alzheimer’s disease. In: 2017 22nd International conference on digital signal processing (DSP), pp 1–5. IEEE

  44. Wang S, Sun J, Mehmood I, Pan C, Chen Y, Zhang Y-D (2020) Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling. Concurr Comput Pract Exper 32(1):e5130

    Google Scholar 

  45. Watabe D, Sai H, Ueda T, Sakai K, Nakamura O (2009) Ica, lda, and gabor jets for robust ear recognition, and jet space similarity for ear detection. Int J Intell Comput Med Sci Image Process 3(1):9–29

    Google Scholar 

  46. Yan P, Bowyer KW (2005) Ear biometrics using 2d and 3d images. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)-Workshops, pp 121–121. IEEE

  47. Youbi Z, Boubchir L, Bounneche MD, Ali-Chérif A, Boukrouche A (2016) Human ear recognition based on multi-scale local binary pattern descriptor and kl divergence. In: 2016 39th International conference on telecommunications and signal processing (TSP), pp 685–688. IEEE

  48. Yuan L, Mu Z Ear recoginition laboratory at ustb, 2004. [Online; accessed 19-February-2017]

  49. Zavar AB, Nixon MS (2008) Robust log-gabor filter for ear biometrics. In: 2008 19th International conference on pattern recognition, pp 1–4. IEEE

Download references

Acknowledgements

We would like to thank the providers of AMI, USTB1, USTB2 and AWE ear databases used in this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meryem Regouid.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

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

Regouid, M., Touahria, M., Benouis, M. et al. Comparative study of 1D-local descriptors for ear biometric system. Multimed Tools Appl 81, 29477–29503 (2022). https://doi.org/10.1007/s11042-022-12700-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12700-x

Keywords

Navigation