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2015 Volume 30
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RESEARCH ARTICLE   Open Access    

Biometric sensors rapid prototyping on field-programmable gate arrays

  • 1.

    Facoltá di Ingegneria

  • 2.

    Istituto di Bioimmagini e Fisiologia Molecolare – Consiglio Nazionale delle Ricerche (IBFM-CNR)

  • 3.

    Dipartimento di Ingegneria Chimica

  • 4.

    Dipartimento di Biopatalogia e Biotecnologie Mediche e Forensi

More Information
  • Abstract: Biometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong and invasive biometric traits, such as fingerprint and iris, have been considered, analyzed, and combined in unimodal and multimodal biometric sensors. Biometric sensor performance has been evaluated using the well-known FVC2002, CASIA, and BATH databases.
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  • Agrawal D., Archambeault B., Rao J. & Rohtagi P.2003. The em-side channel(s). In Workshop on Cryptographic Hardware and Embedded Systems, CHES. LNCS, 2523, 29–45. Springer.

    Google Scholar

    Ambalakat P.2005. Security of Biometric Authentication Systems, 21st Computer Science Seminar, SA1-T1-1, 2,www.rh.edu/rhb/csseminar2005/SessionA1/ambalakat.pdf.

    Google Scholar

    BATH Iris Database website, 2004. http://www.smartsensors.co.uk/irisweb/ (accessed 21 November 2014).

    Google Scholar

    Bonato L. V., Molz R. F., Furtado J. C., Ferrão M. F. & Moraes F. G.2003. (a) Design of a fingerprint system using a hardware/software environment. In Proceedings of the 2003 ACM/SIGDA 11th International Symposium on Field Programmable Gate Arrays, v.1, 240–240, ACM New York press. ISBN: 1-58113-651-X.

    Google Scholar

    Canny J.1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence8, 679–698.

    Google Scholar

    Chinese Academy of Sciences Institute of Automation (CASIA) Iris Image Database (ver. 1.0) 2002. http://www.nlpr.ia.ac.cn/english/irds/Databases/databases.html (accessed 21 November 2014).

    Google Scholar

    Conti V., Militello C., Sorbello F. & Vitabile S.2010. A frequency-based approach for features fusion in fingerprint and iris multimodal biometric identification systems. IEEE Transactions on Systems, Man, and Cybernetics (SMC) Part C: Applications & Reviews40(4), 384–395.

    Google Scholar

    Conti V., Militello C., Vitabile S. & Sorbello F.2009. A multimodal technique for an embedded fingerprint recognizer in mobile payment systems. International Journal of Mobile Information Systems5(2), 105–124.

    Google Scholar

    Daugman J. G.1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence15(11), 1148–1161.

    Google Scholar

    De Mira J.Jr. & Mayer J.2003. Image feature extraction for application of biometric identification of iris – a morphological approach. In Proceedings of the XVI Brazilian Symposium on Computer Graphics and Image Processing, 1, 12–20.

    Google Scholar

    Field D. J.1987. Relations between the statistics of natural images and the response profiles of cortical cells. Journal of the Optical Society of America, 4, 2379–2394.

    Google Scholar

    Fingerprint Acquisition Sensor website, 2002. http://www.biometrika.it/eng/fx2000.html (accessed 21 November 2014).

    Google Scholar

    Fingerprint Verification Competition website2002. http://bias.csr.unibo.it/fvc2002/ (accessed 21 November 2014).

    Google Scholar

    Fons M., Fons F. & Canto E.2006. Design of FPGA-based hardware accelerators for on-line fingerprint matcher systems. Research in Microelectronics and Electronics, 333–336, doi: 10.1109/RME.2006.1689964.

    Google Scholar

    Garcia M. L. & Canto Navarro E. F.2006. FPGA implementation of a ridge extraction fingerprint algorithm based on microblaze and hardware coprocessor. In International IEEE Conference on Field Programmable Logic and Applications, ISBN 1-4244-0312-X, 1–5.

    Google Scholar

    Hough P. V. C.1962. Method and Means for Recognizing Complex Patterns. US Patent 3.069.654.

    Google Scholar

    Iris Acquisition Sensor, 2010. http://uidai.gov.in/biometric-devices.html (accessed 21 November 2014).

    Google Scholar

    Kocher P. C.1999. Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems, Cryptography Research Inc. http://cryptography.com.

    Google Scholar

    Kocher P. C., Jaffe J. & Benjamin Jun B.1999. Differential Power Analysis, Cryptography Research Inc. http://cryptography.com.

    Google Scholar

    Lopez M. & Canto E.2008. FPGA implementation of a minutiae extraction fingerprint algorithm. In IEEE International Symposium on Industrial Electronics, 1920–1925.

    Google Scholar

    Mali M., Novak F. & Biasizzo A.2005. Hardware implementation of AES algorithm. Journal of Electrical Engineering56(9–10), 265–269.

    Google Scholar

    Mane V. M. & Jadhav D. V.2011. Review of multimodal biometrics: applications, challenges and research areas. International Journal of Biometrics and Bioinformatics (IJBB)3(5), 90–95.

    Google Scholar

    Matsumoto T., Matsumoto H., Yamada K. & Hoshino S.2002. Impact of artificial gummy fingers on fingerprint systems. In Proceedings of the SPIE, van Renesse, R. L. (ed.), Optical Security and Counterfeit Deterrence Techniques IV 4677, 275–289.

    Google Scholar

    Mentor Graphics website2008. http://www.mentor.com/products/fpga/handel-c/dk-design-suite/, http://www.mentor.com/products/fpga/handel-c/rc-series-platforms/ (accessed 21 November 2014).

    Google Scholar

    Michener J. R. & Acar T.2000. Security domains: key management in large-scale systems. IEEE Software17(5), 52–58.

    Google Scholar

    Militello C., Conti V., Vitabile S. & Sorbello F.2008. A novel embedded fingerprints authentication system based on singularity points. In Proceedings of the 2nd International Conference on Complex, Intelligent and Software Intensive Systems, ISBN/ISSN: 0-7695-3509-1. IEEE Computer Society, 72–78.

    Google Scholar

    Militello C., Conti V., Vitabile S. & Sorbello F.2009. An embedded module for iris micro-characteristics extraction. In Proceedings of the 3rd International Conference on Complex, Intelligent and Software Intensive Systems. IEEE Computer Society Press, 223–230.

    Google Scholar

    Militello C., Conti V., Vitabile S. & Sorbello F.2010. An embedded iris recognizer for portable and mobile devices. International Journal of Computer Systems Science and Engineering (IJ-CSSE)25(2). Special Issue on Frontiers in Complex, Intelligent and Software Intensive Systems. 119–131.

    Google Scholar

    Militello C., Conti V., Vitabile S. & Sorbello F.2011. Embedded access points for trusted data and resources access in HPC systems. The Journal of Supercomputing55 (1) Special Issue on High Performance Trusted Computing. 4–27.

    Google Scholar

    Miyazawa K., Ito K., Aok T., Kobayashi K. & Katsumata A.2006. An iris recognition system using phase-based image matching, In IEEE International Conference on Image Processing, 325–328.

    Google Scholar

    Nielsen R. & Hamilton B. A.2005. Observations from the deployment of a large scale PKI. In 4th Annual PKI R&D Workshop: Multiple Paths to Trust. NIST, April 19–21.

    Google Scholar

    Niu Z., Zhou K., Jiang H., Yang T. & Yan W.2009. Identification and authentication in large-scale storage systems. In IEEE International Conference on Networking, Architecture, and Storage, 421–427.

    Google Scholar

    Oey M. A., Warnier M., Brazier F. M. T.2010. Security in large-scale open distributed multi-agent systems. In Autonomous Agents, ISBN 978-953-307-089-6, Kordic, V. (ed.). InTech, 107–129. http://www.intechopen.com/articles/show/title/security-in-large-scale-open-distributed-multi-agent-systems.

    Google Scholar

    Ross A. & Jain A.2003. Information fusion in biometrics. Pattern Recognition Letters24, 2115–2125.

    Google Scholar

    Schaumont P., Sakiyama K., Fan Y., Hwang D., Yang S., Hodjat A., Lai B. & Verbauwhede I.2003. Testing ThumbPod: softcore bugs are hard to find. In 8th IEEE International High-Level Design Validation and Test Workshop, ISBN:0-7803-8236-6, 77–82.

    Google Scholar

    Shi J. Q. Z., Zhao X. & Wang Y.2004. A novel fingerprint matching method based on the Hough transform without quantization of the Hough space. In Proceedings of the 3rd International Conference on Image and Graphics, ISBN: 0-7695-2244-0, 262–265.

    Google Scholar

    Snijder M.2006. Security & Privacy in Large Scale Biometric Systems, EC JRC/IPTS, European Biometrics Forum, September 25.

    Google Scholar

    Sung H., Lim J., Park J. & Lee Y.2004. Iris recognition using collarette boundary localization. In Proceedings of the 17th International IEEE Conference on Pattern Recognition, 4, 857–860.

    Google Scholar

    UK Biometrics Working Group (BWG)2003. Biometrics Security Concerns. BWG.

    Google Scholar

    Vitabile S., Conti V., Lentini G. & Sorbello F.2005. An intelligent sensor for fingerprint recognition. In Proceedings of the International Conference on Embedded and Ubiquitous Computing, ISBN: 3-540-30807-5, Lecture Note in Computer Science 3824, 27–36. Springer-Verlag.

    Google Scholar

    Xilinx website2008. http://www.xilinx.com/ (accessed 21 November 2014).

    Google Scholar

    Yoo J. H., Ko J. G., Chung Y. S., Jung S. U., Kim K. H., Moon K. Y. & Chung K.2007. Design of Embedded Multimodal Biometric Systems, 3rd International IEEE Conference on Signal-Image Technologies and Internet-Based System, pp. 1058-1062, DOI 10.1109/SITIS.2007.130.

    Google Scholar

    Zhang H., Yin Y. & Ren G.2004. An improved method for singularity detection of fingerprint images. Book Advances in Biometric Person Authentication. Publisher Springer Berlin/Heidelberg. 3338, 516–524. ISBN 978-3-540-24029-7.

    Google Scholar

  • Cite this article

    Vincenzo Conti, Carmelo Militello, Filippo Sorbello, Salvatore Vitabile. 2015. Biometric sensors rapid prototyping on field-programmable gate arrays. The Knowledge Engineering Review 30(2)201−219, doi: 10.1017/S0269888914000307
    Vincenzo Conti, Carmelo Militello, Filippo Sorbello, Salvatore Vitabile. 2015. Biometric sensors rapid prototyping on field-programmable gate arrays. The Knowledge Engineering Review 30(2)201−219, doi: 10.1017/S0269888914000307
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RESEARCH ARTICLE   Open Access    

Biometric sensors rapid prototyping on field-programmable gate arrays

  • 1.

    Facoltá di Ingegneria

  • 2.

    Istituto di Bioimmagini e Fisiologia Molecolare – Consiglio Nazionale delle Ricerche (IBFM-CNR)

  • 3.

    Dipartimento di Ingegneria Chimica

  • 4.

    Dipartimento di Biopatalogia e Biotecnologie Mediche e Forensi

  • Published online: 25 March 2015
The Knowledge Engineering Review  30,  2015, 30(2): 201−219  |  Cite this article

Abstract: Abstract: Biometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong and invasive biometric traits, such as fingerprint and iris, have been considered, analyzed, and combined in unimodal and multimodal biometric sensors. Biometric sensor performance has been evaluated using the well-known FVC2002, CASIA, and BATH databases.

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    Vincenzo Conti, Carmelo Militello, Filippo Sorbello, Salvatore Vitabile. 2015. Biometric sensors rapid prototyping on field-programmable gate arrays. The Knowledge Engineering Review 30(2)201−219, doi: 10.1017/S0269888914000307
    Vincenzo Conti, Carmelo Militello, Filippo Sorbello, Salvatore Vitabile. 2015. Biometric sensors rapid prototyping on field-programmable gate arrays. The Knowledge Engineering Review 30(2)201−219, doi: 10.1017/S0269888914000307
    shu

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