Abstract
Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature, some various methods have been suggested to solve the occlusion problem. In this paper, two different segmentations of iris are presented. In the first algorithm, a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method, again a circle is located around the pupil with a larger diameter. This time, however, only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31%. The sensitivity of the proposed method is analyzed versus contrast, illumination, and noise as well, where lower sensitivity to all factors is observed when the lower half of the iris is used for recognition.
Highlights
Security and surveillance of information is becoming more and more important recently, in part due to the rapid development of information technology (IT) applications
To implement an automatic iris recognition system, we propose a new algorithm in both iris detection and feature extraction modes
Most rotation invariance methods which are suggested in related papers are achieved by rotating the feature vector before matching [6, 7, 12, 13, 17, 30], and Wildes did it by registering the input image with the model before feature extraction [11]
Summary
Security and surveillance of information is becoming more and more important recently, in part due to the rapid development of information technology (IT) applications. The same may happen for passwords or personal identification numbers. All kinds of these means are not very reliable. Biometrics is the science of recognizing a person based on physical or behavioral characteristics. Fingerprints, voiceprints, retinal blood vessel patterns, face, handwriting, and so forth can be substituted instead of nonbiometric methods for more safety and reliability. Among these biometric characteristics, a fingerprint needs physical contact and can be captured or imitated. Compared with other biometric features, personal authentication based on iris recognition can attain high accuracy due to the rich texture of iris patterns [1,2,3].
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