Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Local feature fusion and SRC-based decision fusion for ear recognition

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

As an emerging biometric technology, human ear recognition has important applications in crime tracking, forensic identification and other fields. In the paper, we propose an effective fusion-based human ear recognition method and describe the algorithm based on three parts: preprocessing, feature extraction and classification decision. First, we employ a weighted distributed adaptive gamma correction (AGCWD)-based image enhancement method for the preprocessing operation. Features are extracted by fusing dense scale invariant feature transform (DSIFT), local binary patterns (LBP) and histogram of gradient directions (HoG), after which we apply two sparse representation-based feature selection methods, namely robust sparse linear discriminant analysis (RSLDA) and inter-class sparsity-based discriminant least square regression (ICS-DLSR), to improve the computational speed. Finally, the two selection features are classified separately using the FDDL-based SRC scheme (FDDL-based SRC), and the two sets of classification results are fused at the decision level to obtain the final decision results. Our algorithm is tested on six commonly used datasets (USTB1, USTB2, USTB3, IITD1, AMI and AWE) and obtained the accuracy of 99.44%, 97.08%, 100%, 100%, 98.14% and 82.90%. The experiments show the superiority of our algorithm compared with other algorithms.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Omara, I., Li, F., Zhang, H., Zuo, W.: A novel geometric feature extraction method for ear recognition. Expert Syst. Appl. 65, 127–135 (2016)

    Article  Google Scholar 

  2. Wang, Z., Yang, J., Zhu, Y.: Review of ear biometrics. Arch. Comput. Methods Eng. 28, 149–180 (2021)

    Article  Google Scholar 

  3. Iannarelli, A.: The Iannarelli system of ear identification. Foundation Press, New York (1964)

    Google Scholar 

  4. Unar, J., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47(8), 2673–2688 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Kasprzak, J.: Forensic otoscopy—new method of human identification. Jurlsprudecija 66(58), 106–109 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  8. Tharewal, S., Gite, H., Kale, K.: 3d face & 3d ear recognition: process and techniques. In: 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), IEEE, Mysore, pp. 1044–1049 (2017)

  9. Murukesh, C., Parivazhagan, A., Thanushkodi, K.: A novel ear recognition process using appearance shape model, fisher linear discriminant analysis and contourlet transform. Proc. Eng. 38, 771–778 (2012)

    Article  Google Scholar 

  10. Prakash, S., Gupta, P.: An efficient ear recognition technique invariant to illumination and pose. Telecommun. Syst. 52(3), 1435–1448 (2011)

    Article  Google Scholar 

  11. Ghoualmi, L., Draa, A., Chikhi, S.: An ear biometric system based on artificial bees and the scale invariant feature transform. Expert Syst. Appl. 57, 49–61 (2016)

    Article  Google Scholar 

  12. Youbi, Z., Boubchir, L., Boukrouche, A.: Human ear recognition based on local multi-scale LBP features with city-block distance. Multimed. Tools Appl. 78(11), 14425–14441 (2018)

    Article  Google Scholar 

  13. Zhang, B., Mu, Z., Zeng, H., Luo, S.: Robust ear recognition via nonnegative sparse representation of Gabor orientation information. Sci. World J. 2014, 1–11 (2014)

    Google Scholar 

  14. Rani, S., Jangilla, S.: Ear recognition using bilinear probabilistic principal component analysis and sparse classifier. In: IEEE Region 10 Conference (TENCON), pp. 22–25. IEEE, Singapore (2016)

  15. Sajadi, S., Fathi, A.: Genetic algorithm based local and global spectral features extraction for ear recognition. Expert Syst. Appl. 159, 113639 (2020)

    Article  Google Scholar 

  16. Dodge, S., Mounsef, J., Karam, L.: Unconstrained ear recognition using deep neural networks. IET Biom. 7(3), 207–214 (2018)

    Article  Google Scholar 

  17. Alshazly, H., Linse, C., Barth, E., Martinetz, T.: Deep convolutional neural networks for unconstrained ear recognition. IEEE Access 8, 170295–170310 (2020)

    Article  Google Scholar 

  18. Abd Almisreb, A., Jamil, N., Din, N.M.: Utilizing AlexNet deep transfer learning for ear recognition. In: 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), pp. 1–5. IEEE, Sabah (2018)

  19. Omara, I., Hagag, A., Ma, G., Abd El-Samie, F.E., Song, E.: A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs. Mach. Vis. Appl. 32(1), 1–14 (2021)

    Article  Google Scholar 

  20. Omara, I., Wu, X., Zhang, H., Du, Y., Zuo, W.: Learning pairwise SVM on hierarchical deep features for ear recognition. IET Biom. 7(6), 557–566 (2018)

    Article  Google Scholar 

  21. Chen, L., Mu, Z.: Partial data ear recognition from one sample per person. IEEE Trans. Hum. Mach. Syst. 46(6), 799–809 (2016)

    Article  Google Scholar 

  22. Alagarsamy, S., Kondappan, S.: Ear recognition system using adaptive approach Runge-Kutta (AARK) threshold segmentation with ANFIS classification. Neural Comput. Appl. 32(15), 10995–11006 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Hansley, E., Segundo, M., Sarkar, S.: Employing fusion of learned and handcrafted features for unconstrained ear recognition. IET Biom. 7(3), 215–223 (2018)

    Article  Google Scholar 

  25. Yuan, L., Liu, W., Li, Y.: Non-negative dictionary based sparse representation classification for ear recognition with occlusion. Neurocomputing 171, 540–550 (2016)

    Article  Google Scholar 

  26. Hezil, N., Boukrouche, A.: Multimodal biometric recognition using human ear and palmprint. IET Biom. 6(5), 351–359 (2017)

    Article  Google Scholar 

  27. Regouid, M., Touahria, M., Benouis, M., Costen, N.: Multimodal biometric system for ECG, ear and iris recognition based on local descriptors. Multimed. Tools Appl. 78(16), 22509–22535 (2019)

    Article  Google Scholar 

  28. Zhu, Q., Mu, Z.: Local and holistic feature fusion for occlusion-robust 3d ear recognition. Symmetry 10(11), 565 (2018)

    Article  Google Scholar 

  29. Zhang, Y., Mu, Z., Yuan, L., Zeng, H., Chen, L.: 3d ear normalization and recognition based on local surface variation. Appl. Sci. 7(1), 104 (2017)

    Article  Google Scholar 

  30. Abaza, A., Bourlai, T.: On ear-based human identification in the mid-wave infrared spectrum. Image Vis. Comput. 31(9), 640–648 (2013)

    Article  Google Scholar 

  31. Claes, P., Reijniers, J., Shriver, M., Snyders, J., Suetens, P., Nielandt, J., Tré, G.D., Vandermeulen, D.: An investigation of matching symmetry in the human pinnae with possible implications for 3d ear recognition and sound localization. J. Anat. 226(1), 60–72 (2014)

    Article  Google Scholar 

  32. Raghavendra, R., Raja, K., Venkatesh, S., Busch, C.: Improved ear verification after surgery—an approach based on collaborative representation of locally competitive features. Pattern Recogn. 83, 416–429 (2018)

    Article  Google Scholar 

  33. Chen, L., Mu, Z., Zhang, B., Zhang, Y.: Ear recognition from one sample per person. PLoS One 10(5), e0129505 (2015)

    Article  Google Scholar 

  34. Paul, P., Gavrilova, M.: Feature and rank level fusion for privacy preserved multi-biometric system. Int. J. Softw. Sci. Comput. Intell. 7(1), 1–17 (2015)

    Article  Google Scholar 

  35. Huang, S., Cheng, F., Chiu, Y.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  36. Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150–1157. IEEE, Kerkyra (1999)

  37. Morales, A., Ferrer, M., Diaz, M., Gonzalez, E.: Analysis of local descriptors features and its robustness applied to ear recognition, In: 2013 47th International Carnahan Conference on Security Technology (ICCST), IEEE, Medellin, pp. 1–5 (2013)

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

    Article  Google Scholar 

  39. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), IEEE, San Diego, pp. 886–893 (2005)

  40. Sarangi, P., Mishra, B., Dehuri, S.: Fusion of PHOG and LDP local descriptors for kernel-based ear biometric recognition. Multimed. Tools Appl. 78(8), 9595–9623 (2018)

    Article  Google Scholar 

  41. Wen, J., Fang, X., Cui, J., Fei, L., Yan, K., Chen, Y., Xu, Y.: Robust sparse linear discriminant analysis. IEEE Trans. Circuits Syst. Video Technol. 29(2), 390–403 (2019)

    Article  Google Scholar 

  42. Wen, J., Xu, Y., Li, Z., Ma, Z., Xu, Y.: Inter-class sparsity based discriminative least square regression. Neural Netw. 102, 36–47 (2018)

    Article  MATH  Google Scholar 

  43. Yang, M., Zhang, L., Feng, X., Zhang, D.: Sparse representation based fisher discrimination dictionary learning for image classification. Int. J. Comput. Vis. 109(3), 209–232 (2014)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  45. Feng, Z., Yang, M., Zhang, L., Liu, Y., Zhang, D.: Joint discriminative dimensionality reduction and dictionary learning for face recognition. Pattern Recogn. 46(8), 2134–2143 (2013)

    Article  Google Scholar 

  46. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  47. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  48. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, pp. 770–778 (2016)

  49. Omara, I., Wu, X., Zhang, H., Du, Y., Zuo, W.: Learning pairwise SVM on deep features for ear recognition. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), IEEE, Wuhan, pp. 341–346 (2017)

  50. Yazdanpanah, A.P., Faez, K.: Gabor-based RCM features for ear recognition, State of the art in Biometrics 221 (2011)

  51. Sultana, M., Paul, P.P., Gavrilova, M.: A novel index-based rank fusion method for occluded ear recognition. In: 2015 International Conference on Cyberworlds (CW), IEEE, Visby, pp. 337–344 (2015)

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Hailong, Z., Junyan, Y.: Combining block DCV and support vector machine for ear recognition. Int. J. Interdiscip. Telecommun. Netw. 8(2), 36–44 (2016)

    Google Scholar 

  55. Sánchez, D., Melin, P., Castillo, O.: Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Inf. Sci. 309, 73–101 (2015)

    Article  Google Scholar 

  56. Melin, P., Sánchez, D., Castillo, O.: Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)

    Article  Google Scholar 

  57. Zarachoff, M., Sheikh, A., Monekosso, D.: 2d multi-band PCA and its application for ear recognition. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE, Krakow, pp. 1–5 (2018)

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

    Article  Google Scholar 

  59. Kumar, A., Wu, C.: Automated human identification using ear imaging. Pattern Recogn. 45(3), 956–968 (2012)

    Article  Google Scholar 

  60. Kumar, A., Chan, T.: Robust ear identification using sparse representation of local texture descriptors. Pattern Recogn. 46(1), 73–85 (2013)

    Article  Google Scholar 

  61. Chan, T., Kumar, A.: Reliable ear identification using 2-d quadrature filters. Pattern Recogn. Lett. 33(14), 1870–1881 (2012)

    Article  Google Scholar 

  62. Rahhal, M.M.A., Mekhalfi, M.L., Guermoui, M., Othman, E., Lei, B., Mahmood, A.: A dense phase descriptor for human ear recognition. IEEE Access 6, 11883–11887 (2018)

    Article  Google Scholar 

  63. Hanmandlu, M.: Robust ear based authentication using local principal independent components. Expert Syst. Appl. 40(16), 6478–6490 (2013)

    Article  Google Scholar 

  64. Mawloud, G., Djamel, M.: Weighted sparse representation for human ear recognition based on local descriptor. J. Electron. Imaging 25(1), 013036 (2016)

    Article  Google Scholar 

  65. Basit, A., Shoaib, M.: A human ear recognition method using nonlinear curvelet feature subspace. Int. J. Comput. Math. 91(3), 616–624 (2013)

    Article  MATH  Google Scholar 

  66. Chowdhury, D., Bakshi, S., Guo, G., Kumar, P.: On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained. J. Med. Syst. 42(1), 1–20 (2017)

    Google Scholar 

  67. Anwar, A., Ghany, K., Elmahdy, H.: Human ear recognition using geometrical features extraction. Proc. Comput. Sci. 65, 529–537 (2015)

    Article  Google Scholar 

  68. Youbi, Z., Boubchir, L., Bounneche, M., Ali, A., Boukrouche, A.: 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), IEEE, Vienna, pp. 685–688 (2016)

  69. Omara, I., Hagag, A., Zuo, W.: Learning LogDet divergence for ear recognition. In: Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications—ICBEA’18. ACM Press, Amsterdam, pp. 69–73 (2018)

  70. Yuan, L., Mu, Z.: Ear recognition based on Gabor features and KFDA. Sci. World J. 2014, 1–12 (2014)

    Google Scholar 

  71. Aleix, M., Robert, B.: The ar face database, Cvc Technical Report 24

  72. Zhang, D., Kong, W., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1041–1050 (2003)

    Article  Google Scholar 

  73. Sun, X., Wang, G., Wang, L., Sun, H., Wei, X.: 3d ear recognition using local salience and principal manifold. Graph. Models 76(5), 402–412 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by National Natural Science Foundation of China (Grant No. 61201421), National cryosphere desert data center (Grant No. E01Z7902 ) and Capability improvement project for cryosphere desert data center of the Chinese Academy of Sciences (Grant No. Y9298302).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Zhaobin Wang, Xiong Gao, Jing Yang & Qizhen Yan

  2. National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China

    Zhaobin Wang & Yaonan Zhang

Authors
  1. Zhaobin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qizhen Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaonan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaobin Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by B-K. Bao.

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

Wang, Z., Gao, X., Yang, J. et al. Local feature fusion and SRC-based decision fusion for ear recognition. Multimedia Systems 28, 1117–1134 (2022). https://doi.org/10.1007/s00530-022-00906-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-022-00906-w

Keywords

Search

Navigation