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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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
Print ISBN: 978-3-319-05475-9
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