Please use this identifier to cite or link to this item:
標題: Iris Recognition Using Possibilistic Fuzzy Matching on Local Features
作者: Tsai, C.C.
Lin, H.Y.
Taur, J.
Tao, C.W.
關鍵字: Gabor filter;iris recognition;possibilistic fuzzy matching (PFM);algorithm;curve;implementation;segmentation;filters
Project: Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics
期刊/報告no:: Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics, Volume 42, Issue 1, Page(s) 150-162.
In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.
ISSN: 1083-4419
DOI: 10.1109/tsmcb.2011.2163817
Appears in Collections:期刊論文

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.