Fusion of Multi-Representation Iris Images For Automatic Person Identification

Abstract

The single biometric system may be inadequate for passive authentication either because of noise in data samples or because of unavailability of a sample at a given time. In order to overcome the limitation of the single biometric, a multi- representation biometric are used. In this paper, we propose a multi-representation biometric system for person identification using Iris modality. This work describes the development of a multi-representation biometric personal identification system based on Minimum Average Correlation Energy Filter (MACE) method (for matching) (fisrt algorithm) and 1D Log Gabor filter (second algorithm). The outputs of each algorithm are combined using the concept of data fusion at matching score level. The experimental results showed that the designed system achieves an excellent identification rate and provides more security than uni-modal biometric system.