Abstract
Segmentation of the iris is one of the key modules of an iris recognition system. For this reason, it is critical to predict failures of this module. In this article we propose a new set of segmentation quality metrics dedicated this problem. We assess the quality of our metrics based on their ability to predict the intrinsic recognition performance of a segmented image. A straightforward fusion procedure then allows generating a global segmentation quality score.
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References
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactionson Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993), http://dx.doi.org/10.1109/34.244676
Grother, P., Tabassi, E.: Performance of biometric quality measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 531–543 (2007)
Kalka, N., Bartlow, N., Cukic, B.: An automated method for predicting iris segmentation failures. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, BTAS 2009, pp. 1–8 (September 2009)
Lee, S.: Quality of Iris Segmentation as a Predictor of Verification Performance. West Virginia University (2007), http://books.google.fr/books?id=HSTnfe9_mVcC
Mayoue, A.: A biometric reference system for iris, osiris version 1.0. Tech. rep., EPH (2008), http://share.int-evry.fr/svnview-eph/
Phillips, P., Scruggs, W., O’Toole, A., Flynn, P., Bowyer, K., Schott, C., Sharpe, M.: Frvt 2006 and ice 2006 large-scale experimental results. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(5), 831–846 (2010)
Proenca, H., Alexandre, L.: Toward covert iris biometric recognition: Experimental results from the nice contests. IEEE Transactions on Information Forensics and Security 7(2), 798–808 (2012)
Sanjay-Gopal, S., Hebert, T.: Bayesian pixel classification using spatially variant finite mixtures and the generalized em algorithm. IEEE Transactions on Image Processing 7(7), 1014–1028 (1998)
Shah, S., Ross, A.: Iris segmentation using geodesic active contours. Transactions on Information Forensics and Security 4, 824–836 (2009), http://dx.doi.org/10.1109/TIFS.2009.2033225
Smola, A.J., Schkopf, B.: A tutorial on support vector regression (2004)
Tabassi, E.: Image specific error rate: A biometric performance metric. In: International Conference on Pattern Recognition (ICPR), pp. 1124–1127 (2010)
Zhang, H., Sun, Z., Tan, T.: Statistics of local surface curvatures for mis-localized iris detection. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4097–4100 (September 2010)
Zhou, Z., Du, Y., Belcher, C.: Transforming traditional iris recognition systems to work in nonideal situations. IEEE Transactions on Industrial Electronics 56(8), 3203–3213 (2009)
Zuo, J., Schmid, N.: An automatic algorithm for evaluating the precision of iris segmentation. In: 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2008), September 29-October 1, pp. 1–6 (2008)
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Lefevre, T., Dorizzi, B., Garcia-Salicetti, S., Lemperiere, N., Belardi, S. (2013). Fusion of Novel Iris Segmentation Quality Metrics for Failure Detection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_12
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DOI: https://doi.org/10.1007/978-3-642-39094-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39093-7
Online ISBN: 978-3-642-39094-4
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