Abstract
This paper proposes an efficient three fold stratified SIFT matching for iris recognition. The objective is to filter wrongly paired conventional SIFT matches. In Strata I, the keypoints from gallery and probe iris images are paired using traditional SIFT approach. Due to high image similarity at different regions of iris there may be some impairments. These are detected and filtered by finding gradient of paired keypoints in Strata II. Further, the scaling factor of paired keypoints is used to remove impairments in Strata III. The pairs retained after Strata III are likely to be potential matches for iris recognition. The proposed system performs with an accuracy of 96.08% and 97.15% on publicly available CASIAV3 and BATH databases respectively. This marks significant improvement of accuracy and FAR over the existing SIFT matching for iris.
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References
Daugman, J.: The importance of being random: Statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)
Kim, J., Cho, S., Choi, J., Marks, R.J.: Iris Recognition Using Wavelet Features. The Journal of VLSI Signal Processing (38), 147–156 (2004)
Monro, D.M., Rakshit, S., Zhang, D.: Dct-based iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 586–595 (2007)
Tisse, C., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proc. of Vision Interface, pp. 294–299 (2002)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 6(2), 91–110 (2004)
Belcher, C., Du, Y.: Region-based sift approach to iris recognition. Optics and Lasers in Engineering 47(1), 139–147 (2009)
Jain, A., Flynn, P., Ross, A.: Handbook of Biometrics. Springer-Verlag New York, Inc., Secaucus (2007)
Bath University Database, http://www.bath.ac.uk/elec-eng/research/sipg/irisweb
Chinese Academy of Sciences’ Institute of Automation (CASIA) Iris Image Database V3.0, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
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© 2011 Springer-Verlag Berlin Heidelberg
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Bakshi, S., Mehrotra, H., Majhi, B. (2011). Stratified SIFT Matching for Human Iris Recognition. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_17
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DOI: https://doi.org/10.1007/978-3-642-22720-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22719-6
Online ISBN: 978-3-642-22720-2
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