Abstract
The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA) to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM). The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.
Highlights
There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for the security
In [45], we developed an iris recognition method based on the support vector machine (SVM), where we used the information of the whole iris region for recognition, and a traditional SVM was used as iris pattern classifiers
We conduct the experimentation on two iris datasets, namely, the iris challenge evaluation (ICE) dataset created by the University of Notre Dame, USA [8], and the CASIA (Chinese Academy of Science—Institute of Automation) dataset [12]
Summary
There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for the security. Iris recognition has been considered as one of the most reliable biometrics technologies in recent years [1, 2]. The human iris is the most important biometric feature candidate, which can be used for differentiating the individuals. Based on the technology developed by Daugman [3, 5,6,7], iris scans have been used in several international airports for the rapid processing of passengers through the immigration which have preregistered their iris images. A matching method was implemented in [23], and its performance was evaluated on a large dataset
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