Abstract
Fingerprint recognition systems are extensively deployed for the authentication in many applications. However, this kind of recognition systems may be spoofed by artificial fingerprints made from various materials. Thus, it is necessary to add a fingerprint liveness detection module to keep this kind of recognition systems on a good level of security. The fingerprint liveness detection (FLD) aims to judge whether a given fingerprint image is captured from a real finger or a spoof one. It is a typical two-class classification problem where the feature extraction is the key step. In this paper, we propose an effective feature extraction method for the FLD problem. The proposed features consist of two components, Weber local binary pattern (WLBP) and circularly symmetric Gabor feature (CSGF), analyzing the fingerprint images in both the spatial and frequency domains. The co-occurrence probabilities of the two components are calculated as the final features. The proposed features are utilized to train SVM classifiers separately on two databases in Fingerprint Liveness Detection Competition 2011 and 2013. Experimental results demonstrate the effectiveness of the proposed method.
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References
Al-Ajlan A (2013) Survey on fingerprint liveness detection. In: International workshop on biometrics and forensics (IWBF), 2013. IEEE, pp 1–5
Antonelli A, Cappelli R, Maio D, Maltoni D (2005) A new approach to fake finger detection based on skin distortion. In: Zhang D, Jain A (eds) Advances in biometrics. Springer, pp 221–228
Antonelli A, Cappelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Secur 1(3):360–373
Bhardwaj I, Londhe ND, Kopparapu SK (2017) A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint. Pattern Recogn 62:214–224
Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Chen J, Shan S, He C, Zhao M, Chen X, Gao W (2010) Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720
Coli P, Marcialis GL, Roli F (2007) Power spectrum-based fingerprint vitality detection. In: IEEE workshop on automatic identification advanced technologies, 2007. IEEE, pp 169–173
Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A Opt Image Sci 2(7):1160
Dubey RK, Goh J, Thing VLL (2016) Fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans Inf Forensics Secur 11(7):1461–1475
Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2012) A high performance fingerprint liveness detection method based on quality related features. Futur Gener Comput Syst 28(1):311–321
Ghiani L, Denti P, Marcialis GL (2012) Experimental results on fingerprint liveness detection. In: Perales FJ, Fisher RB, Moeslund TB (eds) Articulated motion and deformable objects. Springer, pp 210–218
Ghiani L, Marcialis GL, Roli F (2012) Fingerprint liveness detection by local phase quantization. In: International conference on pattern recognition, pp 537–540
Ghiani L, Yambay D, Mura V, Tocco S, Marcialis GL, Roli F, Schuckcrs S (2013) Livdet 2013 fingerprint liveness detection competition 2013. In: International conference on biometrics, pp 208–215
Ghiani L, Yambay DA, Mura V, Marcialis GL, Roli F, Schuckers SA (2017) Review of the fingerprint liveness detection (livdet) competition series: 2009 to 2015. Image Vis Comput 58:110–128
Gottschlich C, Marasco E, Yang AY, Cukic B (2014) Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE international joint conference on biometrics, pp 1–7
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2013) Fingerprint liveness detection based on weber local image descriptor. In: IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS), 2013, pp 46–50
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Trans Inf Forensics Secur 10 (4):849–863
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) Local contrast phase descriptor for fingerprint liveness detection. Pattern Recogn 48(4):1050–1058
Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789
Jia X, Yang X, Cao K, Zang Y, Zhang N, Dai R, Zhu X, Tian J (2014) Multi-scale local binary pattern with filters for spoof fingerprint detection. Inf Sci 268:91–102
Kim W (2017) Fingerprint liveness detection using local coherence patterns. IEEE Signal Process Lett 24(1):51–55
Li C, Duan G, Zhong F (2015) Rotation invariant texture retrieval considering the scale dependence of gabor wavelet. IEEE Trans Image Process 24(8):2344–54
Marasco E, Sansone C (2012) Combining perspiration-and morphology-based static features for fingerprint liveness detection. Pattern Recogn Lett 33(9):1148–1156
Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: 20th international conference on pattern recognition (ICPR), 2010. IEEE, pp 1289–1292
Moon YS, Chen J, Chan K, So K, Woo K (2005) Wavelet based fingerprint liveness detection. Electron Lett 41(20):1112–1113
Nikam SB, Agarwal S (2008) Gabor filter-based fingerprint anti-spoofing. In: Advanced concepts for intelligent vision systems. Springer, pp 1103–1114
Nogueira RF, de Alencar Lotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11 (6):1206–1213
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Park Y, Jang U, Im J, Jang W, Ko D, Lee EC (2017) Fake fingerprint detection based on statistical moments. In: Advanced multimedia and ubiquitous engineering. Springer, pp 43–48
Qin C, Chen X, Ye D, Wang J, Sun X (2016) A novel image hashing scheme with perceptual robustness using block truncation coding. Inf Sci 361–362:84–99
Sousedik C, Busch C (2014) Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biom 3(4):219–233
Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Trefnỳ J, Matas J (2010) Extended set of local binary patterns for rapid object detection. In: Computer vision winter workshop, pp 1–7
Xia Z, Lv R, Zhu Y, Ji P, Sun H, Shi YQ (2017) Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process 11 (2):381–388
Xia Z, Zhang L, Liu D (2016) Attribute-based access control scheme with efficient revocation in cloud computing. China Communications 13(7):92–99
Xia Z, Xiong NN, Vasilakos AV, Sun X (2017) EPCBIR: an efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf Sci 387:195–204
Xia Z, Zhu Y, Sun X, Qin Z, Ren K (2017) Towards privacy-preserving content-based image retrieval in cloud computing. IEEE Trans Cloud Comput PP (99):1–1. https://doi.org/10.1109/TCC.2015.2491933
Yambay D, Ghiani L, Denti P, Marcialis GL, Roli F, Schuckers S (2012) Livdet 2011–fingerprint liveness detection competition 2011. In: 5th IAPR international conference on biometrics (ICB), 2012. IEEE, pp 208–215
Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and pca. China Commun 13(7):60–65
Yuan C, Xia Z, Sun X, Sun D, Lv R (2016) Fingerprint liveness detection using multiscale difference co-occurrence matrix. Opt Eng 55(6):063,111–063,111
Acknowledgements
This work is supported by the NSFC (61672294, 61601236, U1536206, 61502242, 61572258, U1405254, 61373133, 61373132, 61232016), BK20150925, Six peak talent project of Jiangsu Province (R2016L13), NRF-2016R1D1A1B03933294, CICAEET, and PAPD fund. Zhihua Xia is supported by BK21+ program from the Ministry of Education of Korea. The authors would like to thank Mr. Zhaowei Liu for his contribution in this work.
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Xia, Z., Lv, R. & Sun, X. Rotation-invariant Weber pattern and Gabor feature for fingerprint liveness detection. Multimed Tools Appl 77, 18187–18200 (2018). https://doi.org/10.1007/s11042-017-5517-9
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DOI: https://doi.org/10.1007/s11042-017-5517-9