Difference co-occurrence matrix using BP neural network for fingerprint liveness detection
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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.
KeywordsFingerprint liveness detection DCM BP neural network Artificial fingerprints Laplacian operator
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.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
The article does not contain any studies with human participants or animals performed by any of the authors.
- 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 22Google Scholar
- 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 1–6Google Scholar
- 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 1–6Google Scholar
- 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 537–540Google Scholar
- 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 46–50Google Scholar
- 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 1–7Google Scholar
- Jia J, Cai L (2007a) Fake finger detection based on time-series fingerprint image analysis. The interpretation of visual motion. MIT Press, Qingdao, pp 341–345Google Scholar
- 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 309–318Google Scholar
- 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 1–6Google Scholar
- Lu M, Chen Z, Sheng W (2015) Fingerprint liveness detection based on pore analysis. Biometric recognition. Springer, Guangzhou, pp 233–240Google Scholar
- 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 12–23Google Scholar
- 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 1289–1292Google Scholar
- Nixon KA, Rowe RK (2005) Multispectral fingerprint imaging for spoof detection. Proc SPIE Int Soc Opt Eng 5779:214–225Google Scholar
- 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 22–29Google Scholar
- 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-10Google Scholar
- Yuan C, Xia Z, Sun X (2017) Coverless image steganography based on SIFT and BOF without embeding. J Internet Technol 18(2):435–442Google Scholar
- 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):4024–4028Google Scholar