Experiments with an improved iris segmentation algorithm

Author

Liu, X. ; Bowyer, K.W. ; Flynn, P.J.

Author_Institution

Dept. of Comput. Sci. & Eng., Notre Dame Univ., Notre Dame, IN, USA

fYear

2005

fDate

17-18 Oct. 2005

Firstpage

118

Lastpage

123

Abstract

Iris is claimed to be one of the best biometrics. We have collected a large data set of iris images, intentionally sampling a range of quality broader than that used by current commercial iris recognition systems. We have re-implemented the Daugman-like iris recognition algorithm developed by Masek. We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek´s original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate.

Keywords

biometrics (access control); eye; image recognition; image sampling; image segmentation; Daugman-like iris recognition algorithm; biometrics accuracy; eyelid detection; iris image sampling; iris segmentation; rank-one recognition rate; Biometrics; Change detection algorithms; Computer science; Encoding; Eyelids; Image sampling; Image segmentation; Iris recognition; Neodymium; Probes;

fLanguage

English

Publisher

ieee

Conference_Titel

Automatic Identification Advanced Technologies, 2005. Fourth IEEE Workshop on

Conference_Location

Buffalo, NY, USA

Print_ISBN

0-7695-2475-3

Type

conf

DOI

10.1109/AUTOID.2005.21

Filename

1544411