This paper describes a new iris recognition algorithm, which uses a low level of details. Combining statistical classification and elastic boundary fitting, the iris is first localized. Then, the localized iris image is down-sampled by a factor of m, and filtered by a modified Laplacian kernel. Since the output of the Laplacian operator is sensitive to a small shift of the full-resolution iris image, the outputs of the Laplacian operator are computed for all space-shifts. The quantized output with maximum entropy is selected as the final feature representation. Experimentally we showed that the proposed method produces superb performance in iris segmentation and recognition. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Kim, J., Cho, S., Kim, D., & Chung, S. T. (2006). Iris recognition using a low level of details. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4292 LNCS-II, pp. 196–204). Springer Verlag. https://doi.org/10.1007/11919629_21
Mendeley helps you to discover research relevant for your work.