Abstract
This paper proposes an efficient iris based authentication system. Iris segmentation is done using an improved circular hough transform and robust integro-differential operator to detect inner and outer iris boundary respectively. The segmented iris is normalized to polar coordinates and preprocessed using LGBP (Local Gradient Binary Pattern). The corners features are extracted and matched using dissimilarity measure CIOF (Corners having Inconsistent Optical Flow). The proposed approach has been tested on publicly available CASIA 4.0 Interval and Lamp databases consisting of 2,639 and 16,212 images respectively. It has been observed that the segmentation accuracy of more than 99.6% can be achieved on both databases. This paper also provides error classification for wrong segmentation and also determines influential parameters for errors. The proposed system has performed with CRR of 99.75% and 99.87% with an EER of 0.108% and 1.29% on Interval and Lamp databases respectively.
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Nigam, A., Gupta, P. (2013). Iris Recognition Using Consistent Corner Optical Flow. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_27
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DOI: https://doi.org/10.1007/978-3-642-37331-2_27
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