Robustness of subpattern-based approaches to occlusions on iris images

Abstract

The effect of occlusions on holistic and subpattern-based approaches for iris recognition is studied in this paper. The performance analysis of these methods is presented without applying the traditional iris detection methods. Original PCA and subspace LDA methods are used as feature extractors with the combination of the preprocessing techniques of histogram equalization and mean-and-variance normalization in order to nullify the effect of illumination changes which are known to significantly degrade recognition performance. The recognition performance of the holistic approaches is compared with the performance of subpattern-based PCA and subpattern-based subspace LDA approaches To be consistent with the research of others, our work has been tested on three iris databases namely CASIA, UPOL and UBIRIS. The experiments are performed on these three iris databases to demonstrate the recognition performances of the subpattern-based approaches and traditional PCA and subspace LDA approaches.