Abstract
Recently, as biometric technology grows rapidly, the importance of fingerprint spoof detection technique is emerging. In this paper, we propose a technique to detect forged fingerprints using contrast enhancement and Convolutional Neural Networks (CNNs). The proposed method detects the fingerprint spoof by performing contrast enhancement to improve the recognition rate of the fingerprint image, judging whether the sub-block of fingerprint image is falsified through CNNs composed of 6 weight layers and totalizing the result. Our fingerprint spoof detector has a high accuracy of 99.8% on average and has high accuracy even after experimenting with one detector in all datasets.
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Acknowledgments
This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No. R0126-16-1024, Managerial Technology Development and Digital Contents Security of 3D Printing based on Micro Licensing Technology), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B2009595).
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Jang, HU., Choi, HY., Kim, D., Son, J., Lee, HK. (2017). Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_39
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DOI: https://doi.org/10.1007/978-981-10-4154-9_39
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