The Visual Computer

, Volume 35, Issue 10, pp 1393–1410 | Cite as

Fingerprint liveness detection using local quality features

  • Ram Prakash SharmaEmail author
  • Somnath Dey
Original Article


Fingerprint-based recognition is widely deployed in different domains. However, current recognition systems are vulnerable to presentation attack. Presentation attack utilizes an artificial replica of a fingerprint to deceive the sensors. In such scenarios, fingerprint liveness detection is required to ensure the actual presence of a live fingerprint. In this paper, we propose a static software-based approach using quality features to detect the liveness in a fingerprint image. The proposed method extracts eight sensor-independent quality features from the detailed ridge–valley structure of a fingerprint at the local level to form a 13-dimensional feature vector. Sequential Forward Floating Selection and Random Forest Feature Selection are used to select the optimal feature set from the created feature vector. To classify fake and live fingerprints, we have used support vector machine, random forest, and gradient boosted tree classifiers. The proposed method is tested on a publically available database of LivDet 2009 competition. The experimental results demonstrate that the least average classification error of 5.3% is achieved on LivDet 2009 database, exhibiting supremacy of the proposed method over current state-of-the-art approaches. Additionally, we have analyzed the importance of individual features on LivDet 2009 database, and effectiveness of the best-performing features is evaluated on LivDet 2011, 2013, and 2015 databases. The obtained results depict that the proposed approach is able to perform well irrespective of the different sensors and materials used in these databases. Further, the proposed method utilizes a single fingerprint image. This characteristic makes our method more user-friendly, faster, and less intrusive.


Biometrics Presentation attack detection Fingerprint liveness Quality features 



The authors are thankful to SERB (ECR/2017/ 000027), Department of Science Technology, Govt. of India, for providing financial support. Also, we would like to acknowledge the Indian Institute of Technology Indore, for providing the laboratory support and research facilities to carry out this research work.


This research is supported by the Science and Engineering Research Board (SERB) Grant Number ECR/2017/000027.

Compliance with ethical standards

Conflict of interest

Second author of this paper has received research Grants from Science and Engineering Research Board (SERB) and declares no conflict of interest.


  1. 1.
    Abhyankar, A., Schuckers, S.: Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: International Conference on Image Processing, pp. 321–324 (2006)Google Scholar
  2. 2.
    Abhyankar, A., Schuckers, S.: Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognit. 42(3), 452–464 (2009)zbMATHCrossRefGoogle Scholar
  3. 3.
    Abhyankar, A.S., Schuckers, S.C.: A wavelet-based approach to detecting liveness in fingerprint scanners. Proc. SPIE Biom. Technol. Hum. Identif. 5404, 1–9 (2004)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)CrossRefGoogle Scholar
  6. 6.
    Choi, H., Choi, K., Kim, J.: Aliveness detection of fingerprints with image quality analysis. In: International Conference on Electronics, Informations and Communications, pp. 59–62 (2008)Google Scholar
  7. 7.
    Choi, H., Kang, R., Choi, K., Jin, A.T.B., Kim, J.H.: Fake-fingerprint detection using multiple static features. Opt. Eng. 48, 1–13 (2009)Google Scholar
  8. 8.
    Choi, H., Kang, R., Choi, K., Kim, J.: Aliveness detection of fingerprints using multiple static features. Int. J. Comput. Elect. Autom. Control Inf. Eng. 1, 893–898 (2007)Google Scholar
  9. 9.
    Chu, Y., Zhao, L., Ahmad, T.: Multiple feature subspaces analysis for single sample per person face recognition. Vis. Comput. (2018).
  10. 10.
    DeCann, B., Tan, B., Schuckers, S.: A novel region based liveness detection approach for fingerprint scanners. In: Tistarelli, M., Nixon, M.S. (eds.) Advances in Biometrics, pp. 627–636. Springer, Berlin (2009)CrossRefGoogle Scholar
  11. 11.
    Derakhshani, R., Schuckers, S.A., Hornak, L.A., O’Gorman, L.: Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognit. 36(2), 383–396 (2003)CrossRefGoogle Scholar
  12. 12.
    Espinoza, M., Champod, C.: Using the number of pores on fingerprint images to detect spoofing attacks. In: International Conference on Hand-Based Biometrics, pp. 1–5 (2011)Google Scholar
  13. 13.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Galbally, J., Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J.: Fingerprint liveness detection based on quality measures. In: First IEEE International Conference on Biometrics, Identity and Security (BIdS), pp. 1–8 (2009)Google Scholar
  15. 15.
    Galbally, J., Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J.: A high performance fingerprint liveness detection method based on quality related features. Future Gener. Comput. Syst. 28(1), 311–321 (2012)CrossRefGoogle Scholar
  16. 16.
    Galbally, J., Marcel, S., Fierrez, J.: Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans. Image Process. 23(2), 710–724 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Ghiani, L., Denti, P., Marcialis, G.L.: Experimental results on fingerprint liveness detection. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds.) Articulated Motion and Deformable Objects, pp. 210–218. Springer, Berlin (2012)CrossRefGoogle Scholar
  18. 18.
    Ghiani, L., Hadid, A., Marcialis, G.L., Roli, F.: Fingerprint liveness detection using binarized statistical image features. In: IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6 (2013)Google Scholar
  19. 19.
    Ghiani, L., Marcialis, G.L., Roli, F.: Fingerprint liveness detection by local phase quantization. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 537–540 (2012)Google Scholar
  20. 20.
    Ghiani, L., Yambay, D., Mura, V., Tocco, S., Marcialis, G.L., Roli, F., Schuckcrs, S.: Livdet 2013 fingerprint liveness detection competition 2013. In: International Conference on Biometrics (ICB), pp. 1–6 (2013)Google Scholar
  21. 21.
    Ghiani, L., Yambay, D.A., Mura, V., Marcialis, G.L., Roli, F., Schuckers, S.A.: Review of the fingerprint liveness detection (LivDet) competition series: 2009 to 2015. Image Vis. Comput. 58, 110–128 (2017)CrossRefGoogle Scholar
  22. 22.
    Gottschlich, C., Marasco, E., Yang, A.Y., Cukic, B.: Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE International Joint Conference on Biometrics, pp. 1–7 (2014)Google Scholar
  23. 23.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University (2010)Google Scholar
  24. 24.
    Huang, Q., Chang, S., Liu, C., Niu, B., Tang, M., Zhou, Z.: An evaluation of fake fingerprint databases utilizing SVM classification. Pattern Recognit. Lett. 60–61, 1–7 (2015)CrossRefGoogle Scholar
  25. 25.
    Jia, J., Cai, L., Zhang, K., Chen, D.: A new approach to fake finger detection based on skin elasticity analysis. In: Lee, S.-W., Li, S.Z. (eds.) Advances in Biometrics, pp. 309–318. Springer, Berlin (2007)CrossRefGoogle Scholar
  26. 26.
    Jia, X., Yang, X., Zang, Y., Zhang, N., Dai, R., Tian, J., Zhao, J.: Multi-scale block local ternary patterns for fingerprints vitality detection. In: International Conference on Biometrics (ICB), pp. 1–6 (2013)Google Scholar
  27. 27.
    Kim, W.: Fingerprint liveness detection using local coherence patterns. IEEE Signal Process. Lett. 24(1), 51–55 (2017)CrossRefGoogle Scholar
  28. 28.
    Lee, H., Maeng, H., Bae, Y.: Fake finger detection using the fractional Fourier transform. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) Biometric ID Management and Multimodal Communication, pp. 318–324. Springer, Berlin (2009)CrossRefGoogle Scholar
  29. 29.
    Li, C., Zhou, W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1419–1429 (2015)CrossRefGoogle Scholar
  30. 30.
    Lim, E., Toh, K., Suganthan, P., Jiang, X., Yau, W.: Fingerprint image quality analysis. In: International Conference on Image Processing (ICIP), vol. 5, pp. 1241–1244 (2004)Google Scholar
  31. 31.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, Berlin (2009). IncorporatedzbMATHCrossRefGoogle Scholar
  32. 32.
    Manivanan, N., Memon, S., Balachandran, W.: Automatic detection of active sweat pores of fingerprint using highpass and correlation filtering. Electron. Lett. 46(18), 1268–1269 (2010)CrossRefGoogle Scholar
  33. 33.
    Marasco, E., Sansone, C.: Combining perspiration and morphology based static features for fingerprint liveness detection. Pattern Recognit. Lett. 33(9), 1148–1156 (2012)CrossRefGoogle Scholar
  34. 34.
    Marcialis, G.L., Lewicke, A., Tan, B., Coli, P., Grimberg, D., Congiu, A., Tidu, A., Roli, F., Schuckers, S.: First international fingerprint liveness detection competition—livdet 2009. Image Anal. Process. ICIAP 2009, 12–23 (2009)Google Scholar
  35. 35.
    Marcialis, G.L., Roli, F., Tidu, A.: Analysis of fingerprint pores for vitality detection. In: 20th International Conference on Pattern Recognition, pp. 1289–1292 (2010)Google Scholar
  36. 36.
    Moon, Y.S., Chen, J.S., Chan, K.C., So, K., Woo, K.C.: Wavelet based fingerprint liveness detection. Electron. Lett. 41(20), 1112–1113 (2005)CrossRefGoogle Scholar
  37. 37.
    Mura, V., Ghiani, L., Marcialis, G.L., Roli, F., Yambay, D.A., Schuckers, S.A.: Livdet 2015 fingerprint liveness detection competition 2015. In: IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–6 (2015)Google Scholar
  38. 38.
    Nikam, S.B., Agarwal, S.: Fingerprint liveness detection using curvelet energy and co-occurrence signatures. In: 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation, pp. 217–222 (2008)Google Scholar
  39. 39.
    Nikam, S.B., Agarwal, S.: Texture and wavelet-based spoof fingerprint detection for fingerprint biometric systems. In: First International Conference on Emerging Trends in Engineering and Technology, pp. 675–680 (2008)Google Scholar
  40. 40.
    Nikam, S.B., Agarwal, S.: Ridgelet-based fake fingerprint detection. Neurocomputing 72(10), 2491–2506 (2009)CrossRefGoogle Scholar
  41. 41.
    Nikam, S.B., Agarwal, S.: Curvelet-based fingerprint anti-spoofing. Signal Image Video Process. 4(1), 75–87 (2010)CrossRefGoogle Scholar
  42. 42.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)zbMATHCrossRefGoogle Scholar
  43. 43.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  44. 44.
    Olsen, M.A., Smida, V., Busch, C.: Finger image quality assessment features: definitions and evaluation. IET Biom. 5(2), 47–64 (2016)CrossRefGoogle Scholar
  45. 45.
    Olsen, M.A., Xu, H., Busch, C.: Gabor filters as candidate quality measure for NFIQ 2.0. In: 5th IAPR International Conference on Biometrics (ICB), pp. 158–163 (2012)Google Scholar
  46. 46.
    Pudil, P., Novoviov, J., Kittler, J.: Floating search methods infeature selection. Pattern Recognit. Lett. 15(11), 1119–1125 (1994)CrossRefGoogle Scholar
  47. 47.
    Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, pp. 582–588 (1999)Google Scholar
  48. 48.
    Shahzad, M., Nadarajah, M., Noor, A., Balachadran, W., Boulgouris, N.V.: Fingerprint sensors: liveness detection and hardware solutions. Sens. Biosens. MEMS Technol. Appl. 136(1), 35–49 (2012)Google Scholar
  49. 49.
    Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. Proc. Int. Conf. Image Process. 2, 37–40 (2005)Google Scholar
  50. 50.
    Tan, B., Schuckers, S.: Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners. In: Proceedings of SPIE: Biometric Technology for Human Identification III, vol. 6202, pp. 1–10 (2006)Google Scholar
  51. 51.
    Tan, B., Schuckers, S.: New approach for liveness detection in fingerprint scanners based on valley noise analysis. J. Electron. Imaging 1(17), 011009 (2008)CrossRefGoogle Scholar
  52. 52.
    Wang, Z., Miao, Z., Wu, Q.M.J., Wan, Y., Tang, Z.: Low-resolution face recognition: a review. Vis. Comput. 30(4), 359–386 (2014)CrossRefGoogle Scholar
  53. 53.
    Xia, Z., Lv, R., Zhu, Y., Ji, P., Sun, H., Shi, Y.Q.: Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process. 11(2), 381–388 (2017)CrossRefGoogle Scholar
  54. 54.
    Yambay, D., Ghiani, L., Denti, P., Marcialis, G.L., Roli, F., Schuckers, S.: Livdet 2011 fingerprint liveness detection competition 2011. In: 5th IAPR International Conference on Biometrics (ICB), pp. 208–215 (2012)Google Scholar
  55. 55.
    Yuan, C., Sun, X., Lv, R.: Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun. 13(7), 60–65 (2016)CrossRefGoogle Scholar
  56. 56.
    Zhang, Y., Fang, S., Xie, Y., Xu, T.: Fake fingerprint detection based on wavelet analysis and local binary pattern. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds.) Biometric Recognition, pp. 191–198. Springer, Berlin (2014)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology IndoreIndoreIndia

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