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Biometric sensors rapid prototyping on field-programmable gate arrays

Published online by Cambridge University Press:  25 March 2015

Vincenzo Conti ,
Carmelo Militello ,
Filippo Sorbello  and
Salvatore Vitabile
Vincenzo Conti
Affiliation:
Facoltá di Ingegneria, e Architettura, Università degli Studi di Enna KORE, viale delle Olimpiadi, 94100, Enna, Italy e-mail: [email protected]
Carmelo Militello
Affiliation:
Istituto di Bioimmagini e Fisiologia Molecolare – Consiglio Nazionale delle Ricerche (IBFM-CNR), UOS Cefalu’, C.da Pietrapollastra-Pisciotto – 90015 Cefalu’ (PA), Italy e-mail: [email protected]
Filippo Sorbello
Affiliation:
Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Universita’ degli Studi di Palermo, 90128 Palermo, Italy e-mail: [email protected]
Salvatore Vitabile
Affiliation:
Dipartimento di Biopatalogia e Biotecnologie Mediche e Forensi, Universita’ degli Studi di Palermo, via del Vespro, 90127 Palermo, Italy e-mail: [email protected]
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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.

Type
Articles
Information
The Knowledge Engineering Review , Volume 30 , Issue 2: Intelligent Computing in Large–Scale Systems , March 2015 , pp. 201 - 219
Copyright
© Cambridge University Press, 2015 

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References

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.CrossRefGoogle 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 Intelligence 8, 679–698.CrossRefGoogle ScholarPubMed
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 & Reviews 40(4), 384–395.CrossRefGoogle 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 Systems 5(2), 105–124.CrossRefGoogle Scholar
Daugman, J. G. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161.CrossRefGoogle 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.CrossRefGoogle Scholar
Fingerprint Acquisition Sensor website, 2002. http://www.biometrika.it/eng/fx2000.html (accessed 21 November 2014).Google Scholar
Fingerprint Verification Competition website 2002. 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.CrossRefGoogle 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 Engineering 56(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 website 2008. 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 Software 17(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 Supercomputing 55 (1) Special Issue on High Performance Trusted Computing. 4–27.CrossRefGoogle 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 Letters 24, 2115–2125.CrossRefGoogle 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.CrossRefGoogle Scholar
Xilinx website 2008. 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.CrossRefGoogle Scholar