Abstract
With the growing use of fingerprint identification systems in recent years, preventing fingerprint identification systems from being spoofed by artificial fake fingerprints has become a critical problem. In this paper, we put forward a novel method to detect fingerprint liveness based on BP neural network, which is used for the first time in the fingerprint liveness detection. Moreover, different from traditional detection methods, we propose a scheme to construct the input data and corresponding category labels. More effective and efficient texture features of fingerprints, which are used as the input data of the BP neural network, are computed to improve classification performance and obtain a better pre-trained network model. After a variety of preprocessing operations and image compression operations, gradient values in the horizontal and vertical directions are computed by using Laplacian operator, and difference co-occurrence matrices are constructed from the obtained gradient values. Then, the input data of neural network model are built based on two DCMs. The pre-trained neural network models with diverse neuron nodes are learnt. Different experiments based on different parameters for the BP neural network have been conducted. Finally, classification accuracy of testing fingerprints is predicted based on the pre-trained networks. Experimental results on the LivDet 2013 show that the classification performance of our proposed method is effective and meanwhile provides a better detection accuracy compared with the majority of previously published results.
Similar content being viewed by others
References
Abhyankar A, Schuckers S (2009) Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recogn 42(3):452–464
Antonelli A, Cappelli R, Maio D, Maltoni D (2005) A new approach to fake finger detection based on skin distortion. Advances in Biometrics. Springer, Hong Kong, 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
Choi H, Kang R, Choi K, Kim J (2007) Aliveness detection of fingerprints using multiple static features. In: Proceedings of world academy of science, engineering and technology, vol 22
Darlow LN, Connan J (2015) Efficient internal and surface fingerprint extraction and blending using optical coherence tomography. J Appl Opt 54(31):9258–9268
Dubey RK, Goh J, Things VLL (2016) Fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans Inf Forensics Secur 11(7):1461–1475
Gu B, Sun X, Sheng V (2016) Structural Minimax Probability Machine. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2016.2544779
Ghiani L, Hadid A, Marcialis GL, Roil F (2013a) Fingerprint liveness detection using binarized statistical image features. In: IEEE 6th international conference on biometrics: theory, applications and systems, Washington DC, USA, pp 1–6
Ghiani L, Yambay D, Mura V, Tocco S, Marcialis GL, Roli F , Schuckcrs S (2013b) Livdet 2013 fingerprint liveness detection competition 2013, Biometrics (ICB). In: International conference on IEEE, Madrid, Spain, pp 1–6
Ghiani L, Marcialis G L, Roli F (2015) Fingerprint liveness detection by local phase quantization. In: Proceedings of the 21st international conference on pattern recognition (ICPR), Langkawi, Malaysia, pp 537–540
Gragnaniello D, Poggi G, Sansone C, 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. IEEE, Napoli, Italy, pp 46–50
Gragnaniello D, Poggi G, Sansone C, Verdoliva L (2015) Local contrast phase descriptor for fingerprint liveness detection. Pattern Recogn 48(4):1050–1058
Gottschlich C (2016) Convolution comparison pattern: an efficient local image descriptor for fingerprint liveness detection. Plos One 11(2):e0148552
Gottschlich C, Marasco E, Yang AY, Cukic B (2014) Fingerprint liveness detection based on histograms of invariant gradients. In: International joint conference on biometrics (IJCB), 2014. IEEE, FL, USA, pp 1–7
Jia J, Cai L (2007a) Fake finger detection based on time-series fingerprint image analysis. The interpretation of visual motion. MIT Press, Qingdao, pp 341–345
Jia J, Cai L, Zhang K, Chen D (2007b) A new approach to fake finger detection based on skin elasticity analysis. Advances in biometrics. Springer, Berlin Heidelberg, pp 309–318
Jia X, Yang X, Zang Y, Zhang N, Dai R, Tian J, Zhao J (2016) Multi-scale block local ternary patterns for fingerprints vitality detection. In: International conference on biometrics, Halmstad, Sweden, pp 1–6
Kim S, Park B, Song BS, Yang S (2016) Deep belief network based statistical feature learning for fingerprint liveness detection. Pattern Recogn Lett 77:58–65
Li J, Li X, Yang B, Sun X (2015) Segmentation-based Image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518
Lu M, Chen Z, Sheng W (2015) Fingerprint liveness detection based on pore analysis. Biometric recognition. Springer, Guangzhou, pp 233–240
Manivanan N, Memon S, Balachandran W (2010) Automatic detection of active sweat pores of fingerprint using highpass and correlation filtering. Electron Lett 46(18):1268–1269
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, Lewicke A, Tan B, Coli P, Grimberg D, Congiu A, Schuckers S (2009) First international fingerprint liveness detection competition livdet 2009. In: Image analysis and processing CICIAP 2009, Springer, Berlin, Heidelberg, Vietri sul Mare, Italy, pp 12–23
Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: 20th International conference on pattern recognition (ICPR), 2010. IEEE, Istanbul, Turkey, 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) Local binary pattern and waveletbased spoof fingerprint detection. Int J Biom 1(2):141–159
Nixon KA, Rowe RK (2005) Multispectral fingerprint imaging for spoof detection. Proc SPIE Int Soc Opt Eng 5779:214–225
Nogueira RF, Lotufo RDA, Machado RC (2014) Evaluating software-based fingerprint liveness detection using convolutional networks and local binary patterns. Biometric measurements and systems for security and medical applications. IEEE, Rome, Italy, pp 22–29
Nogueira RF, Lotufo R, Machado RC (2016) Fingerprint liveness detection using convolutional neural networks. IEEE Trans Inf Forensics Secur 11(6):1206–1213
Pan Z, Jin P, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast reference frame selection based on content similarity for low complexity HEVC encoder. J Vis Commun Image Represent 40(Part B):516–524
Reddy PV, Kumar A, Rahman S, Mundra TS (2008) A new antispoofing approach for biometric devices. IEEE Trans Biomed Circuits Syst 2(4):328–337
Tan B, Schuckers S (2006) Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners. Proc SPIE Int Soc Opt Eng 23(12):62020A–62020A-10
Tan B, Schuckers S (2010) Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise. Pattern Recogn 43(8):2845–2857
Xia Z, Wang X, Sun X, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(2):340–352
Yuan C, Xia Z, Sun X, Sun D, Lv R (2016a) Fingerprint liveness detection using multiscale difference co-occurrence matrix. Opt Eng 55(6):063111-1–063111-10
Yuan C, Sun X, Sun D, Lv R (2016b) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65
Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on SIFT and BOF without embeding. J Internet Technol 18(2):435–442
Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028
Acknowledgements
This work is supported by the NSFC (U1536206, 61672294, U1405254, 61502242 and 61602253), BK20150925, Fund of Jiangsu Engineering Center of Network Monitoring (KJR1402), Fund of MOE Internet Innovation Platform (KJRP1403), Fund of Jiangsu Postgraduate Research and Innovation Program Project (KYCX17_0899), State Scholarship Fund (201708320316), CICAEET and the PAPD fund.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
The article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
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
Yuan, C., Sun, X. & Wu, Q.M.J. Difference co-occurrence matrix using BP neural network for fingerprint liveness detection. Soft Comput 23, 5157–5169 (2019). https://doi.org/10.1007/s00500-018-3182-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-018-3182-1