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A novel method based on deep learning for aligned fingerprints matching

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Abstract

In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.

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Notes

  1. http://caffe.berkeleyvision.org/.

  2. http://tensorflow.org/.

  3. https://github.com/lyhucas/TestMatching

  4. NIST Biometric Image Software (NBIS), http://www.nist.gov/itl/iad/ig/nbis.cfm.

References

  1. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition. Springer, London

    Book  Google Scholar 

  2. Hong L, Wan Y, Jain AK (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789

    Article  Google Scholar 

  3. Jain AK, Hong L, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19(4):302–314

    Article  Google Scholar 

  4. Feng J, Jain AK (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33(2):209–223

    Article  Google Scholar 

  5. Bazen AM, Verwaaijen GTB, Gerez SH, Veelenturf LPJ, Van der Zwaag BJ (2000) A correlation-based fingerprint verification system. In: Proceedings of the ProRISC2000 workshop on circuits, systems and signal processing, pp 205–213

  6. Bazen AM, Gerez SH, Veelenturf L, Zwaag BV, Verwaaijen G (2000) A correlation-based fingerprint verification system. Stw Technology Foundation 31(5):652–655

    Google Scholar 

  7. Ross A, Reisman J, Jain AK (2002) Fingerprint matching using feature space correlation. International Workshop Copenhagen on Biometric Authentication 2359:48–57

    Article  Google Scholar 

  8. Cavusoglu A, Gorgunoglu S (2007) A robust correlation based fingerprint matching algorithm for verification. J Appl Sci 7(21):3286–3291

    Article  Google Scholar 

  9. Ghaddab MH, Jouini K, Korbaa O (2017) Fast and accurate fingerprint matching using expanded delaunay triangulation. In: Proceedings of the IEEE/ACS international conference on computer systems and applications, pp 751–758

  10. Peralta D, Galar M, Triguero I, Miguel-Hurtado O, Benitez JM, Herrera F (2014) Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms. Eng Appl Artif Intell 32(32):37–53

    Article  Google Scholar 

  11. Li Q, Nguyen VH, Liu J, Kim H (2017) Multi-feature score fusion for fingerprint recognition based on neighbor minutiae boost. IEIE Transactions on Smart Processing and Computing 6(6):387–400

    Article  Google Scholar 

  12. Kumar R, Chandra P, Hanmandlu M (2016) A robust fingerprint matching system using orientation features. J Inf Process Syst 12(1):83–99

    Google Scholar 

  13. Guo JM, Liu YF, Chang JY, Lee JD (2014) Fingerprint classification based on decision tree from singular points and orientation field. Expert Syst Appl 41(2):752–764

    Article  Google Scholar 

  14. Galar M, Derrac J, Peralta D, Triguero I, Paternain D, et al. (2015) A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models. Knowl-Based Syst 81:76–97

    Article  Google Scholar 

  15. Galar M, Derrac J, Peralta D, Triguero I, Paternain D, et al. (2015) A survey of fingerprint classification Part II: experimental analysis and ensemble proposal. Knowl-Based Syst 81:98–116

    Article  Google Scholar 

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the advances in neural information processing systems, vol 60, pp 1097–1105

  17. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  18. Szegedy C, Liu W, Jia Y, Sermanet P, et al. (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  19. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  20. Luo X, Tian J, Wu Y (2000) A minutiae matching algorithm in fingerprint verification. In: Proceedings of the IEEE international conference on pattern recognition, vol 4, pp 833–836

  21. Jiang X, Yau WY (2000) Fingerprint minutiae matching based on the local and global structures. In: Proceedings of the IEEE international conference on pattern recognition, vol 2, pp 1038–1041

  22. Ouyang W, Zeng X, Wang X, Qiu S, Luo P, Tian Y, et al. (2017) Object detection with deformable part based convolutional neural networks. IEEE Trans Pattern Anal Mach Intell 39(7):1320– 1334

    Article  Google Scholar 

  23. Xue D, Wu L, Hong Z, Guo S, Gao L, Wu Z, et al. (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell 48(11):4232–4246

    Article  Google Scholar 

  24. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Proceedings of the advances in neural information processing systems, vol 60, pp 1988–1996

  25. Tripathi BK (2017) On the complex domain deep machine learning for face recognition. Appl Intell 47(3):1–15

    MathSciNet  Google Scholar 

  26. Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737

    Article  Google Scholar 

  27. Cho SB, Won HH (2007) Cancer classification using ensemble of neural networks with multiple significant gene subsets. Appl Intell 26(3):243–250

    Article  Google Scholar 

  28. Acharya UR, Fujita H, Shu LO, Hagiwara Y, Tan JH, Adam M, et al. (2018) Deep convolutional neural network for the automated diagnosis of congestive heart failure using ecg signals. Appl Intell 49(1):16–27

  29. Fujita H, Cimr D (2019) Computer Aided detection for fibrillations and flutters using deep convolutional neural network. Inf Sci 486:231–239

  30. Wang R, Han C, Guo T (2016) A novel fingerprint classification method based on deep learning. In: Proceedings of the IEEE international conference on pattern recognition, pp 931– 936

  31. Nguyen DL, Cao K, Jain AK (2018) Robust minutiae extractor: integrating deep networks and fingerprint domain knowledge. In: Proceedings of the international conference on biometrics, pp 9–16

  32. Liu Y, Zhou B, Han C, Guo T, Qin J (2018) A method for singular points detection based on faster-RCNN. Appl Sci 8(10):1853

    Article  Google Scholar 

  33. Qin J, Tang S, Han C, Guo T (2017) Partial fingerprint matching via phase-only correlation and deep convolutional neural network. In: Proceedings of the international conference on neural information processing, pp 602–611

  34. Cappelli R, Ferrara M, Maltoni D (2011) Fingerprint indexing based on minutia cylinder-code. IEEE Trans Pattern Anal Mach Intell 33(5):1051–1057

    Article  Google Scholar 

  35. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  36. Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). arXiv:1511.07289

  37. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. Comput Sci 3(4):212–223

    Google Scholar 

  38. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov RR (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  39. Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the international conference on artificial intelligence and statistics, vol 15, pp 315–323

  40. Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: Proceedings of the European conference on computer vision, pp 499–515

  41. Chopra S, Hadsell R, Lecun Y (2005) Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, pp 539–546

  42. Norouzi M, Fleet DJ, Salakhutdinov R (2012) Hamming distance metric learning. In: Proceedings of the advances in neural information processing systems, vol 2, pp 1061–1069

  43. Jia Y, Shelhamer E, Donahue J, Karayev S, et al. (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM international conference on multimedia, pp 675–678

  44. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) Fvc2000: Fingerprint verification competition. IEEE Trans Pattern Anal Mach Intell 24(3):402–412

    Article  Google Scholar 

  45. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) FVC 2002: second fingerprint verification competition. In: Proceedings of the IEEE international conference on pattern recognition, pp 811–814

  46. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2004) Fvc2004: third fingerprint verification competition. Lect Notes Comput Sci 3072(2):1–7

    Google Scholar 

  47. Watson CI, Wilson C (1992) Nist special database 4, Fingerprint database national institute of standards and technology

  48. Watson CI (1992) Nist special database 14, Fingerprint database national institute of standards and technology

  49. Cappelli R, Erol A, Maio D, Maltoni D (2000) Synthetic fingerprint-image generation. In: Proceedings of the IEEE international conference on pattern recognition, pp 471–474

  50. Cappelli R, Maio D, Maltoni D (2002) Synthetic fingerprint-database generation. In: Proceedings of the IEEE international conference on pattern recognition, pp 744–747

  51. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z et al (2016) TensorFlow: large-scale machine learning on heterogeneous systems. arXiv:1603.04467. Software available from http://tensorflow.org

  52. Cappelli R, Ferrara M, Maltoni D (2010) Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 32(12):2128–2141

    Article  Google Scholar 

  53. He Y, Tian J, Li L, Chen H, Yang X (2006) Fingerprint matching based on global comprehensive similarity. IEEE Trans Pattern Anal Mach Intell 28(6):850–862

    Article  Google Scholar 

Download references

Acknowledgment

This research was funded by the State Key Program of National Natural Science Foundation of China under grant number 11731013 and 11331012, and by the National Natural Science Foundation of China under grant number 11571014.

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Authors and Affiliations

  1. University of Chinese Academy of Sciences (UCAS), Beijing, China

    Yonghong Liu, Baicun Zhou, Congying Han, Tiande Guo & Jin Qin

  2. Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing, China

    Congying Han & Tiande Guo

Authors
  1. Yonghong Liu
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  2. Baicun Zhou
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  3. Congying Han
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  4. Tiande Guo
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  5. Jin Qin
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Correspondence to Congying Han.

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Liu, Y., Zhou, B., Han, C. et al. A novel method based on deep learning for aligned fingerprints matching. Appl Intell 50, 397–416 (2020). https://doi.org/10.1007/s10489-019-01530-4

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