Abstract
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.
Similar content being viewed by others
References
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)
Abhyankar, A., Schuckers, S.: Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognit. 42(3), 452–464 (2009)
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)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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)
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)
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)
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)
Chu, Y., Zhao, L., Ahmad, T.: Multiple feature subspaces analysis for single sample per person face recognition. Vis. Comput. (2018). https://doi.org/10.1007/s00371-017-1468-4
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)
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)
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)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Kim, W.: Fingerprint liveness detection using local coherence patterns. IEEE Signal Process. Lett. 24(1), 51–55 (2017)
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)
Li, C., Zhou, W., Yuan, S.: Iris recognition based on a novel variation of local binary pattern. Vis. Comput. 31(10), 1419–1429 (2015)
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)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, Berlin (2009). Incorporated
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)
Marasco, E., Sansone, C.: Combining perspiration and morphology based static features for fingerprint liveness detection. Pattern Recognit. Lett. 33(9), 1148–1156 (2012)
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)
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)
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)
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)
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)
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)
Nikam, S.B., Agarwal, S.: Ridgelet-based fake fingerprint detection. Neurocomputing 72(10), 2491–2506 (2009)
Nikam, S.B., Agarwal, S.: Curvelet-based fingerprint anti-spoofing. Signal Image Video Process. 4(1), 75–87 (2010)
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)
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)
Olsen, M.A., Smida, V., Busch, C.: Finger image quality assessment features: definitions and evaluation. IET Biom. 5(2), 47–64 (2016)
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)
Pudil, P., Novoviov, J., Kittler, J.: Floating search methods infeature selection. Pattern Recognit. Lett. 15(11), 1119–1125 (1994)
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)
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)
Tabassi, E., Wilson, C.L.: A novel approach to fingerprint image quality. Proc. Int. Conf. Image Process. 2, 37–40 (2005)
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)
Tan, B., Schuckers, S.: New approach for liveness detection in fingerprint scanners based on valley noise analysis. J. Electron. Imaging 1(17), 011009 (2008)
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)
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)
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)
Yuan, C., Sun, X., Lv, R.: Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun. 13(7), 60–65 (2016)
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)
Acknowledgements
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.
Funding
This research is supported by the Science and Engineering Research Board (SERB) Grant Number ECR/2017/000027.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sharma, R.P., Dey, S. Fingerprint liveness detection using local quality features. Vis Comput 35, 1393–1410 (2019). https://doi.org/10.1007/s00371-018-01618-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-018-01618-x