Abstract
Minutiae used in most fingerprint recognition devices is robust to presentation attack, but generates a high false match rate. Thus, it is applied along with orientation map or skeleton images. There has been plenty of research on security vulnerability of minutiae, whereas few research has been conducted on orientation map or skeleton images. This study analyzes vulnerability of presentation attack for skeleton images. For this purpose, it proposes a new algorithm of recovering fingerprints with the use of machine learning and skeleton image features of fingerprints. In the proposed method, we suggest the new machine learning Pix2Pix model to generate more natural images. The suggested model is developed in the way of adding a latent vector to the conventional image-to-image translation model Pix2Pix. In the experiment, fingerprints were recovered with the use of the proposed Pix2Pix model, and it was found that a fingerprint recognition device which recognized the recovered fingerprints had a high success rate of recognition. Therefore, it was proved that a fingerprint recognition device using skeleton images as well was vulnerable to presentation attack. It is expected that the algorithm proposed in this study will be very useful to many different application areas related to image processing, including biometrics, fingerprint recognition and recovery, and image surveillance.
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References
Almajmaie L, Ucan ON, Bayat O (2019) Fingerprint recognition system based on modified multi-connect architecture (MMCA). Cognit. Syst. Res 58:107–113. https://doi.org/10.1016/j.cogsys.2019.05.004
Bansal R, Sehgal P, Bedi P (2011) Minutiae Extraction from Fingerprint Images - a review. IJSCI International Jounal of Computer Science Issues 8(5):74–85
Bontrager, P., Roy, A., Togelius, J., Memon, N., Ross, A. (2018) DeepMasterPrints: generating MasterPrints for dictionary attacks via latent variable evolution. In Proc. of the IEEE 9th international conference on biometrics theory, applications and systems (BTAS), Redondo Beach, USA, 1-9. DOI: https://doi.org/10.1109/BTAS.2018.8698539
Cai H, Yang Z, Cao X, Xia W, Xu X (2014) New iterative triclass thresholding technique in image segmentation. IEEE trans. Image process 23(3):1038–1046. https://doi.org/10.1109/TIP.2014.2298981
Ding, S., Wallin, A. (2019) Towards recovery of conditional vectors from conditional generative adversarial networks. Pattern Recogn. Lett. 122:66-72. DOI: 10.1016/j.patrec.2019.02.020
Du C, Chen B, Xu B, Guo D, Liu H (2019) Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition. Signal process 158:176–189. https://doi.org/10.1016/j.sigpro.2019.01.006
Espinoza M, Champod C, Margot P (2011) Vulnerabilities of fingerprint reader to fake fingerprints attacks. Forensic Sci. Int 204(1-3):41–49. https://doi.org/10.1016/j.forsciint.2010.05.002
Feng J, Jain AK (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33(2):209–223. https://doi.org/10.1109/TPAMI.2010.77
Ge Y, Yang D, Lu J, Li B, Zhang X (2013) Active appearance models using statistical characteristics of Gabor based texture representation. J. Vis. Comm. Image represent 24(5):627–634. https://doi.org/10.1016/j.jvcir.2013.04.011
Hamidi H (2019) An approach to develop the smart health using internet-of-things and authentication based on biometric technology. Future Generat. Comput. Syst. 91:434–449. https://doi.org/10.1016/j.future.2018.09.024
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S. (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In Proc. of the 31st international conference on neural information processing systems, 6629-6640.
Huang B, Chen W, Wu X, Lin C-L, Suganthan PN (2018) High-quality face image generated with conditional boundary equilibrium generative adversarial networks. Pattern Recogn. Lett 111:72–79. https://doi.org/10.1016/j.patrec.2018.04.028
Huang G, Wan Z, Liu X, Hui J, Zhang Z (2019) Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images. Neurocomputing. 332:215–223. https://doi.org/10.1016/j.neucom.2018.12.050
Ibtehaz, N., Sohel Rahman, M. (2020) MultiResUNet: rethinking the U-net architecture for multimodal biomedical image segmentation. Neural networks, 121:74-87. DOI: https://doi.org/10.1016/j.neunet.2019.08.025
Jia, S., Guo, G., Xu, Z. (2020) A survey on 3D mask presentation attack detection and countermeasures. Pattern Recogn. 98, article 107032. DOI: 10.1016/j.patcog.2019.107032
Kho JB, Kim J, Kim I-J, Teoh ABJ (2019) Cancelable fingerprint template design with randomized non-negative least squares. Pattern Recogn 91:245–260. https://doi.org/10.1016/j.patcog.2019.01.039
Kim J, Moon J, Hwang E, Kang P (2019) Recurrent inception convolution neural network for multi short-term load forecasting. Energ. Build 194:328–341. https://doi.org/10.1016/j.enbuild.2019.04.034
Lee W, Choa S, Choi H, Kim J (2017) Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners. Expert Syst. Appl 87:183–198. https://doi.org/10.1016/j.eswa.2017.06.019
Lee, S., Choi, J.-G., Park, J.-H., Kim, G.-Y. (2019) Synthesizing fingerprint from pattern type analysis features using cGAN. In Proc. of the world congress on information technology applications and services (WITC), Jeju, Korea.
Leng L, Teoh ABJ (2015) Alignment-free row-co-occurrence cancelable palmprint fuzzy vault. Pattern Recogn 48(7):2290–2303. https://doi.org/10.1016/j.patcog.2015.01.021
Leng L, Zhang J (2013) PalmHash code vs. PalmPhasor code. Neurocomputing 1082:1–12. https://doi.org/10.1016/j.neucom.2012.08.028
Leng L, Teoh ABJ, Li M, Khan MK (2014) Analysis of correlation of 2D palm hash code and orientation range suitable for transposition. Neurocomputing 1315:377–387. https://doi.org/10.1016/j.neucom.2013.10.005
Leng L, Teoh ABJ, Li M, Khan MK (2014) A remote cancelable palmprint authentication protocol based on multi-directional two-dimensional PalmPhasor-fusion. Secur. Comm. Network 7(11):1860–1871. https://doi.org/10.1002/sec.900
Li, J., Feng, J., Jay Kuo, C.-C. (2018) Deep convolutional neural network for latent fingerprint enhancement. Signal process. Image Comm. 60:52-63. DOI: 10.1016/j.image.2017.08.010
Liu X, Bai Y, Luo Y, Yang Z, Liu Y (2019) Iris recognition in visible spectrum based on multi-layer analogous convolution and collaborative representation. Pattern Recogn. Lett 117:66–73. https://doi.org/10.1016/j.patrec.2018.12.003
Mishkin D, Sergievskiy N, Matas J (2017) Systematic evaluation of convolution neural network advances on the Imagenet. Comput. Vis. Image understand 161:11–19. https://doi.org/10.1016/j.cviu.2017.05.007
Paliwal N, Vanjani P, Liu J-W, Saini S, Sharma A (2019) Image processing-based intelligent robotic system for assistance of agricultural crops. International Journal of Social and Humanistic Computing. 3:191–204. https://doi.org/10.1504/IJSHC.2019.101602
Peralta D, Garcia S, Benitez JM, Herrera F (2017) Minutiae-based fingerprint matching decomposition: methodology for big data frameworks. Inform. Sci 408:198–212. https://doi.org/10.1016/j.ins.2017.05.001
Riaz F, Hassan A, Rehman S, Qamar U (2013) Texture classification using rotation- and scale-invariant Gabor texture features. IEEE signal process. Lett. 20(6):607–610. https://doi.org/10.1109/LSP.2013.2259622
Ross, A., Shah, J., Jain, K. (2005) Towards reconstructing fingerprints from minutiae points. In Proc. of the SPIE conference on biometric Technology for Human Identification II, Orlando, USA, 5779:68-80. DOI: https://doi.org/10.1117/12.604477
Sannidhan MS, Prabhu GA, Robbins DE, Shasky C (2019) Evaluating the performance of face sketch generation using generative adversarial networks. Pattern Recogn. Lett 128:452–458. https://doi.org/10.1016/j.patrec.2019.10.010
Sharma RP, Dey S (2019) Two-stage quality adaptive fingerprint image enhancement using fuzzy C-means clustering based fingerprint quality analysis. Image Vis. Comput 83-84:1–16. https://doi.org/10.1016/j.imavis.2019.02.006
Sunday MA, Patel PA, Dodd MD, Gauthier I (2019) Gender and hometown population density interact to predict face recognition ability. Vis Res 163:14–23. https://doi.org/10.1016/j.visres.2019.08.006
Talab AMA, Huang Z, Xi F, HaiMing L (2016) Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik - international journal for light and Electron optics 127(3):1030–1033. https://doi.org/10.1016/j.ijleo.2015.09.147
Unar JA, Seng WC, Abbasi A (2014) A review of biometric technology along with trends and prospects. Pattern Recogn 47(8):2673–2688. https://doi.org/10.1016/j.patcog.2014.01.016
Wang S, Deng G, Hu J (2017) A partial Hadamard transform approach to the design of cancelable fingerprint templates containing binary biometric representations. Pattern Recogn 61:447–458. https://doi.org/10.1016/j.patcog.2016.08.017
Yang H-M, Lim D-W, Choi Y-S, Kang J-G, Kim I-H, Lin A, Jung J-W (2019) Image-based human sperm counting method. International Journal of Social and Humanistic Computing 3:148–157. https://doi.org/10.1504/IJSHC.2019.101598
Youssef R, Sevestre-Ghalila S, Ricordeau A, Benazza A (2016) Self noise and contrast controlled thinning of gray images. Pattern Recogn 57:97–114. https://doi.org/10.1016/j.patcog.2016.03.033
Yu L, He Z, Cao Q (2010) Gabor texture representation method for face recognition using the gamma and generalized Gaussian models. Image Vis. Comput 28(1):177–187. https://doi.org/10.1016/j.imavis.2009.05.012
Yuan X-C, Wu L-S, Peng Q (2015) An improved Otsu method using the weighted object variance for defect detection. Appl. Surf. Sci 349:472–484. https://doi.org/10.1016/j.apsusc.2015.05.033
Zhang Y-D, Pan C, Sun J, Tang C (2018) Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. Journal of computational science 28:1–10. https://doi.org/10.1016/j.jocs.2018.07.003
Zhao Y, Takaki S, Luong H-T, Yamagishi J, Saito D, Minematsu N (2018) Wasserstein GAN and waveform loss-based acoustic model training for multi-speaker text-to-speech synthesis systems using a wavenet vocoder. IEEE access 6:60478–60488. https://doi.org/10.1109/ACCESS.2018.2872060
Zuniga AG, Florindo JB, Bruno OM (2014) Gabor wavelets combined with volumetric fractal dimension applied to texture analysis. Pattern Recogn. Lett 36(135-143). https://doi.org/10.1016/j.patrec.2013.09.023
Acknowledgments
This work was supported by the Soongsil University Research Fund of 2017. In addition, this research was supported by Global Infrastructure Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(NRF-2016K1A3A1A19945935).
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Lee, S., Jang, SW., Kim, D. et al. A Novel Fingerprint Recovery Scheme using Deep Neural Network-based Learning. Multimed Tools Appl 80, 34121–34135 (2021). https://doi.org/10.1007/s11042-020-09157-1
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DOI: https://doi.org/10.1007/s11042-020-09157-1