Robust Iris Templates for Efficient Person Identification
Iris recognition is seen as a highly reliable biometric technology. The performance of iris recognition is severely impacted when encountering irises captured in realistic conditions. The selection of the features subset and the classification is an important issue for iris biometrics. In this paper we propose new methods for feature extraction and template creation during enrollment to improve the performance of iris recognition systems. The experiments are based on storing i) multiple templates (template group) for a user ii) Single template by taking average mean of multiple templates iii) Single template calculated from multiple templates using Direct Linear Discriminant Analysis (DLDA). We used CASIA Iris Interval database for our experiments. Experiments report significant improvement in the performance of iris recognition.
KeywordsFeature Extraction Biometric Identification Wavelet Transform template creation
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