An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition

This research proposed a multi-modal person authentication system developed by feature level fusion of iris recognition and retina recognition. The reasons for choosing iris and retina as biometric characteristics are they provide the highest level of uniqueness, performance, universality, and circumvention. The `curse-of-dimensionality' problem introduced in feature level fusion which was the main limitation of the prior works in this field, was minimized to a great extent by applying Principal Component Analysis (PCA) on the augmented feature template. To validate this approach, iris and retina images obtained from `IITD' and `DRIVE' datasets respectively are used. The recognition rate for the proposed multi-modal biometric system was 98.37% whereas it is 96.74% and 94.56% for iris recognition and retina recognition respectively.