Iris Segmentation and Recognition | Zendy

Jaemin Kim | Zendy; Seongwon Cho | Zendy

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Iris Segmentation and Recognition

Author(s) -

Jaemin Kim,

Seongwon Cho

Publication year - 2002

Publication title -

international journal of fuzzy logic and intelligent systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.296

H-Index - 9

eISSN - 2093-744X

pISSN - 1598-2645

DOI - 10.5391/ijfis.2002.2.3.227

Subject(s) - iris (biosensor) , artificial intelligence , segmentation , pattern recognition (psychology) , iris recognition , similarity (geometry) , node (physics) , piecewise , computer science , boundary (topology) , signal (programming language) , piecewise linear function , mathematics , set (abstract data type) , computer vision , image (mathematics) , geometry , biometrics , mathematical analysis , physics , acoustics , programming language

A new iris segmentation and recognition method is described. Combining a statistical classification and elastic boundary fitting, the iris is first segmented robustly and accurately. Once the iris is segmented, one-dimensional signals are computed in the iris and decomposed into multiple frequency bands. Each decomposed signal is approximated by a piecewise linear curve connecting a small set of node points. The node points represent features of each signal. The similarity measture between two iris images is the normalized cross-correlation coefficients between simplified signals.

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