A Synthetic Fusion Rule Based on FLDA and PCA for Iris Recognition Using 1D Log-Gabor Filter

Abstract

Iris recognition is one of the most useful methods to identify or verify people in biometric recognition systems. Iris patterns contain many features that distinguish people from each other. In this paper, a novel iris recognition method is proposed based on the fusion of Fisher Linear Discriminate Analysis (FLDA) with embedding Principal Component Analysis (PCA) method. In this work, firstly we use 1D Log-Gabor to elicit the iris features from an approximation part. Secondly, we obtain an appropriate degree of clarity for the iris with fusion of FLDA/PCA to eliminate the optical reflections on the iris image. Experiments of our proposed algorithm are performed on the CASIA V1 database. The results of our proposed approach show a good performance with recognition rate up to 99.99%.