Abstract
Iris biometric for personal identification is based on capturing an eye image and obtaining features that will help in identifying a human being. However, captured images may not be of good quality due to variety of reasons e.g. occlusion, blurred images etc. Thus, it is important to assess image quality before applying feature extraction algorithm in order to avoid insufficient results. In this paper, iris quality assessment research is extended by analysing the effect of entropy, mean intensity, area ratio, occlusion, blur, dilation and sharpness of an iris image. Firstly, each parameter is estimated individually, and then fused to obtain a quality score. A fusion method based on principal component analysis (PCA) is proposed to determine whether an image is good or not. To test the proposed technique; Chinese Academy of Science Institute of Automation (CASIA), Internal Iris Database (IID) and University of Beira Interior (UBIRIS) databases are used.
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References
Belcher, C., Du, Y.: A selective feature information approach for iris image-quality measure. IEEE Transactions on Information Forensics and Security, 572–577 (2008)
Chen, Y., Dass, S.C., Jain, A.K.: Localized Iris Inage Quality Using 2-D Wavelets. In: Proc. IEEE International Conference Biometrics, pp. 387–381 (2006)
Crete, F., Dolmiere, T., Ladret, P., Nicolas, M.: The blur effect: perception and estimation with a new no-reference perceptual blur metric. Human Vision and Electronic Image in XIIÂ 6492, 64920I (2007)
Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Fairchild, M.D.: Color Appearance Models. In: Slides from a tutorial at the IST/SID 12th Color Imaging Conference (2004)
Fatukasi, O., Kittler, J., Poh, N.: Quality controlled multi-modal fusion of biometric experts. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 881–890. Springer, Heidelberg (2007)
Gulmire, K., Ganorkar, S.: Iris recognition using Gabor wavelet. International Journal of Engineering 1(5) (2012)
Kalka, N.D., Dorairaj, V., Shah, Y.N., Schmid, N.A., Cukic, B.: Image quality assessment for iris biometric. In: Proceedings of the 24th Annual Meeting of the Gesellscha it Klassikation, pp. 445–452. Springer (2002)
Li, Y.H., Savvides, M.: An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Transactions on Pattern Analysis Machine Intelligence 35(4), 784–796 (2013)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)
Masek, L.: Recognition of human iris patterns for biometric identification. PhD thesis
Tabassi, E.: Biometric Quality Standards, NIST, Biometric Consortium (2009)
Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analysers. Neural Computation 11(2), 443–482 (1999)
Yalamanchili, R.K.: Occlussion Metrics. West virginia University (2011)
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Makinana, S., Malumedzha, T., Nelwamondo, F.V. (2014). Iris Image Quality Assessment Based on Quality Parameters. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_58
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DOI: https://doi.org/10.1007/978-3-319-05476-6_58
Publisher Name: Springer, Cham
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