Abstract
Authentication using fingerprints is widely deployed in various applications to ensure a secure and efficient method for access control. However, fingerprint recognition systems can be deceived by spoofing attacks. Therefore, it is necessary to ensure the security of fingerprint-based recognition system using liveness detection. The work presented in this paper evaluates the potential of various handcrafted texture features under cross-dataset, cross-sensor, cross-material, unknown-material, and combined datasets experimental scenarios. We have considered Binarized Statistical Image Features (BSIF), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), Local Contrast Phase Descriptor (LCPD), and Rotation Invariant Co-occurrence among adjacent Local Binary Pattern (RicLBP) for liveness detection of fingerprint images. The performance of these descriptors against novel spoof materials, different sensors, and different acquisition environments reflect their robustness under real world attack scenarios. The experimental evaluations are performed on LivDet 2011, 2013, and 2015 databases using Support Vector Machine (SVM) classifier. The experimental evaluation shows that LCPD and WLD are the most effective descriptors for liveness detection under diverse testing conditions. The comparative performance evaluation of these handcrafted texture features with learning based features also indicate their effectiveness in real world attack scenarios. Experimental evaluation using the combination of best performing LCPD and WLD features further improve the performance of fingerprint liveness detection. The experimental outcome of the current research clearly indicates the superiority of handcrafted local texture descriptor in the real world presentation attack scenarios. Also, it is advantageous to use local texture descriptor as they provide a simple and faster approach for fingerprint liveness detection in real world applications of fingerprint recognition systems.





Similar content being viewed by others
References
Antonelli A, Cappelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forens Secur 1(3):360–373
Baldisserra D, Franco A, Maio D, Maltoni D (2005) Fake fingerprint detection by odor analysis. Adv Biom 265–272
Bian W, Xu D, Li Q, Cheng Y, Jie B, Ding X (2019) A survey of the methods on fingerprint orientation field estimation. IEEE Access 7:32644–32663
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chen J, Shan S, He C, Zhao G, Pietikainen 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, pp 169–173
Ghiani L, Denti P, Marcialis G L (2012) Experimental results on fingerprint liveness detection. In: Articulated motion and deformable objects, pp 210–218
Ghiani L, Marcialis GL, Roli F (2012) Fingerprint liveness detection by local phase quantization. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 537–540
Ghiani L, Hadid A, Marcialis GL, Roli F (2013) Fingerprint liveness detection using binarized statistical image features. In: IEEE sixth international conference on biometrics: theory, applications and systems (BTAS), pp 1–6
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 (ICB), pp 1–6
Ghiani L, Hadid A, Marcialis GL, Roli F (2017) Fingerprint liveness detection using local texture features. IET Biom 6(3):224–231
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, pp 46–50
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2014) Wavelet-markov local descriptor for detecting fake fingerprints. Electron Lett 50(6):439–441
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) An investigation of local descriptors for biometric spoofing detection. IEEE Trans Inf Forens Secur 10(4):849–863
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) Local contrast phase descriptor for fingerprint liveness detection. Pattern Recognit 48 (4):1050–1058
Hsu C W, Chang C C, Lin C J (2010) A practical guide to support vector classification. Tech. rep., Department of Computer Science National Taiwan University
Jia X, Yang X, Zang Y, Zhang N, Dai R, Tian J, Zhao J (2013) Multi-scale block local ternary patterns for fingerprints vitality detection. In: International conference on biometrics (ICB), pp 1–6
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. New Sensing and Processing Technologies for Hand-based Biometrics Authentication
Kannala J, Rahtu E (2012) Bsif: binarized statistical image features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp 1363–1366
Kim W (2017) Fingerprint liveness detection using local coherence patterns. IEEE Signal Process Lett 24(1):51–55
Kim W, Jung C (2016) Local accumulated smoothing patterns for fingerprint liveness detection. Electron Lett 52:1912–1914(2)
Kumpituck S, Li D, Kunieda H, Isshiki T (2017) Fingerprint spoof detection using wavelet based local binary pattern. In: Eighth international conference on graphic and image processing (ICGIP 2016), vol 10225, pp 260–267
Lapsley P D, Lee J A, Pare D F Jr, Hoffman N (1998) Anti-fraud biometric scanner that accurately detects blood flow. U.S. Patent
Liao S, Zhu X, Lei Z, Zhang L, Li S Z (2007) Learning multi-scale block local binary patterns for face recognition. In: Advances in biometrics, pp 828–837
Maenpaa T (2003) The local binary pattern approach to texture analysis—extensions and applications (phd thesis) University of Oulu
Maenpaa T, Pietikainen M (2003) Multi-scale binary patterns for texture analysis. In: Image analysis, pp 885–892
Marasco E, Ross A (2014) A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput Surv 47(2):1–36
Marcel S, Nixon M S, Li S Z (2014) Handbook of biometric anti-spoofing: trusted biometrics under spoofing attacks, Springer Publishing Company Incorporated, London
Marcialis G, Lewicke A, Tan B, Coli P, Grimberg D, Congiu A, Tidu A, Roli F, Schuckers S (2009) First international fingerprint liveness detection competition—livdet 2009. In: Image analysis and processing—ICIAP 2009, pp 12–23
Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: 20th International conference on pattern recognition, pp 1289–1292
Mehboob R, Dawood H, Dawood H, Ilyas M U, Guo P, Banjar A (2018) Live fingerprint detection using magnitude of perceived spatial stimuli and local phase information. J Electron Imaging 27(5):1–13
Mura V, Ghiani L, Marcialis G L, Roli F, Yambay D A, Schuckers S A (2015) Livdet 2015 fingerprint liveness detection competition 2015. In: IEEE 7th international conference on biometrics theory, applications and systems (BTAS), pp 1–6
Nikam SB, Agarwal S (2008) 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
Nikam SB, Agarwal S (2008) Wavelet energy signature and glcm features-based fingerprint anti-spoofing. In: 2008 International conference on wavelet analysis and pattern recognition, vol 2, pp 717–723
Nikam S B, Agarwal S (2010) Curvelet-based fingerprint anti-spoofing. Signal Image Video Process 4(1):75–87
Nogueira RF, de Alencar Lotufo R, Campos Machado R (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213
Nosaka R, Ohkawa Y, Fukui K (2012) Feature extraction based on co-occurrence of adjacent local binary patterns. In: Advances in image and video technology, pp 82–91
Nosaka R, Suryanto C H, Fukui K (2013) Rotation invariant co-occurrence among adjacent lbps. In: Computer vision—ACCV 2012 workshops, pp 15–25
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
Ojansivu V, Rahtu E, Heikkila J (2008) Rotation invariant local phase quantization for blur insensitive texture analysis. In: 2008 19th International conference on pattern recognition, pp 1–4
Rahtu E, Heikkilä J, Ojansivu V, Ahonen T (2012) Local phase quantization for blur-insensitive image analysis. Image Vis Comput 30(8):501–512
Schölkopf B, Williamson R, Smola A, Shawe-Taylor J, Platt J (1999) Support vector method for novelty detection. In: Proceedings of the 12th international conference on neural information processing systems, pp 582–588
Tan B, Schuckers S (2006) Liveness detection for fingerprint scanners based on the statistics of wavelet signal processing. In: 2006 Conference on computer vision and pattern recognition workshop (CVPRW’06), pp 26–26
Tan B, Schuckers S C (2008) New approach for liveness detection in fingerprint scanners based on valley noise analysis. J Electron Imaging 17:1–9
Wang C, Li K, Wu Z, Zhao Q (2015) A dcnn based fingerprint liveness detection algorithm with voting strategy. In: Biometric recognition, pp 241–249
Xia Z, Lv R, Zhu Y, Ji P, Sun H, Shi Y Q (2017) Fingerprint liveness detection using gradient-based texture features. Signal Image Video Process 11:381–388
Yambay D, Ghiani L, Denti P, Marcialis G L, Roli F, Schuckers S (2012) Livdet 2011—fingerprint liveness detection competition 2011. In: 5th IAPR international conference on biometrics (ICB) , pp 208–215
Acknowledgements
The authors would like to acknowledge SERB (ECR/2017/000027), Department of Science Technology, Govt. of India for providing financial support for this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have 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. A comparative study of handcrafted local texture descriptors for fingerprint liveness detection under real world scenarios. Multimed Tools Appl 80, 9993–10012 (2021). https://doi.org/10.1007/s11042-020-10136-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10136-9