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Abstract
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Article Information:
A New Iris Segmentation Method Based on Improved Snake Model and Angular Integral Projection
Ann A. Jarjes, Kuanquan Wang and Ghassan J. Mohammed
Corresponding Author: Ann A. Jarjes
Submitted: 2011 May, 21
Accepted: 2011 June, 20
Published: 2011 June, 25 |
Abstract:
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Segmenting iris region is fundamental for iris-based biometric systems. The overall performance of
an iris recognition system is highly dependent on accurate iris segmentation. In this paper, a new algorithm for
iris segmentation is proposed towards more accurate and efficient segmentation, it detects the precise pupil
contour and localizes the limbic boundary. An improved snake model is presented, wherein a new external
energy function is designed. This external energy is computed based on the Angular Integral Projection
Function (AIPF). First, the AIPF is combined with the improved snake to detect the pupil boundary. For that,
pupil boundary points are detected by using the AIPF, and circle fitting is followed to localize the circular pupil
boundary giving the initial snake contour. Then, the precise pupil contour is detected by deploying the improved
snake. Second, as another contribution of this work, the limbic boundary is localized by combining the AIPF
with a technique to extract outlier boundary points based on Mahalanobis distance. Experimental results on
CASIA V3.0 iris image database show that the improved snake model is comparable with conventional snake
model, and the whole segmentation performance of the proposed algorithm outperforms those of other wellknown
existing methods in both terms of accuracy and processing time.
Key words: Active contour model, angular integral projection, biometrics, Iris segmentation, boundary detection, external energy field,
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Cite this Reference:
Ann A. Jarjes, Kuanquan Wang and Ghassan J. Mohammed, . A New Iris Segmentation Method Based on Improved Snake Model and Angular Integral Projection. Research Journal of Applied Sciences, Engineering and Technology, (06): 558-568.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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