Abstract
The rich random structure of the iris, non-invasiveness and temporal stability properties makes it one of the most powerful biometric trait for person identification. Iris recognition techniques have demonstrated very low false acceptance rates and very high matching efficiency in large iris datasets. However, iris recognition systems face several challenges including non-uniform lighting conditions, occlusion of the eye by the eyelid, variation of head pose (causes dimensional inconsistencies of iris part), the reflection of surrounding objects and many others. The paper has addressed some of the reported issues of iris recognition technique by exploring new feature extraction technique and devising a new symbolic modelling approach for feature representation and classification, along with other classification techniques namely k-NN, SVM and PNN. The symbolic approach employed in the proposed research to represent iris feature reduces the dimensionality of feature space and also reduces the actual time required for iris recognition. The symbolic approach with new feature extraction technique has attained recognition accuracy of 100%, 98.26%, 99.25% and 96.67% on CASIA 1.1, CASIA 4.0 Interval, SGGSIE&T and the newly formed VISA Iris datasets respectively. Additionally, authors deployed previous algorithms and their findings of the results are discussed in comparison with proposed techniques.
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