Abstract
Authentication systems enable the verification of claimed identity; on computer systems these are typically password-based. However, such systems are vulnerable to numerous attack vectors and are responsible for a large number of security breaches. Biometrics is now commonly investigated as an alternative to password-based systems. There are numerous biometric characteristics that can be used for authentication purposes, each with different levels of accuracy and positive and negative implementation factors. The objective of the current study was to investigate fingerprint recognition utilizing Artificial Neural Networks (ANNs) as a classifier. An innovative representation method for fingerprint features was developed to facilitate verification by ANNs. For each participant, the method required the alignment of their fingerprint samples (based on extracted local features), and the selection of 8 of these aligned features common to their samples. The six attributes belonging to each of the selected features were used for ANN input. Unlike the common usage, each participant had one dedicated ANN trained to recognize only their fingerprint samples. Experimental results returned a false acceptance rate (FAR) of 0.0 and a false rejection rate (FRR) of 0.0022, which were comparable to (and in some cases, slightly better than) other research efforts in the field.
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References
Schneier, B.: Secrets & Lies: Digital Security in a Networked World. John Wiley and Sons, Inc. (2000)
Jain, A., Ross, A., Prabhakar, S.: An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)
Yager, N., Amin, A.: Fingerprint Verification Based on Minutiae Features: A Review. Pattern Analysis Applications 7(1), 94–113 (2004)
Galton, F.: Finger Prints. Macmillan and Company (1892)
Abernethy, M.: User Authentication Incorporating Feature Level Data Fusion of Multiple Biometric Characteristics. Murdoch University, Australia (2011), http://researchrepository.murdoch.edu.au/10609/
Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer (2003)
Jain, A., Hong, L., Bolle, R.: On-Line Fingerprint Verification. IEEE Transactions on Pattern Recognition and Machine Learning 19(4), 302–314 (1997)
Jiang, X., Yau, W.: Fingerprint Minutiae Matching Based on the Local and Global Structures. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, pp. 1038–1041 (2000)
Lee, D., Choi, K., Kim, J.: A Robust Fingerprint Matching Algorithm Using Local Alignment. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 3, pp. 803–806 (2002)
He, Y., Tian, J., Luo, X., Zhang, T.: Image Enhancement and Minutiae Matching in Fingerprint Verification. Pattern Recognition Letters 24, 1349–1360 (2003)
Tong, X., Huang, J., Tang, X., Shi, D.: Fingerprint Minutiae Matching Using The Adjacent Feature Vector. Pattern Recognition Letters 26, 1337–1345 (2005)
Qi, J., Wang, Y.: A Robust Fingerprint Matching Method. Pattern Recognition 38, 1665–1671 (2005)
Jie, Y., Yi Fang, Y., Renjie, Z., Qifa, S.: Fingerprint Minutiae Matching Algorithm for Real Time System. Pattern Recognition 39(1), 143–146 (2006)
Kumar, R., Deva Vikram, B.: Fingerprint Matching Using Multi-Dimensional ANN. Engineering Applications of Artificial Intelligence 23, 222–228 (2010)
Sadi, M., Kanij, T.: Fingerprint Verification: A Comparison of Three Approaches. In: Defense Science Research Conference and Expo IEEE, pp. 1–5 (2011)
Arantes, M., Ide, A., Saito, J.: A System for Fingerprint Minutiae Classification and Recognition. In: Proceedings 9th International Conference on Neural Information Processing, vol. 5, pp. 2474–2478 (2002)
Xiangrong, L., Guohui, W., Xiangjiang, L.: Neural Network Based Automatic Fingerprints Classification Algorithm. In: International Conference of Information Science and Management Engineering, vol. 1, pp. 94–96 (2010)
Van Wamelen, P., Li, Z., Iyengar, S.: A Fast Expected Time Algorithm for The 2-D Point Pattern Matching Problem. Pattern Recognition 37, 1699–1711 (2004)
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Abernethy, M., Rai, S.M. (2013). An Innovative Fingerprint Feature Representation Method to Facilitate Authentication Using Neural Networks. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_85
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DOI: https://doi.org/10.1007/978-3-642-42042-9_85
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