Recognizing partially occluded irises using subpattern-based approaches

In this study, iris recognition in the presence of partial occlusions is investigated using holistic and subpattern-based approaches. Principal Component Analysis (PCA) and subspace Linear Discriminant Analysis (ssLDA) methods are used as feature extractors to recognize iris images. In order to eliminate the effect of illumination changes, histogram equalization and mean-and-variance normalization techniques are used. The recognition performance of the holistic approaches is compared with the performance of subpattern-based approaches spPCA, mPCA and subpattern-based ssLDA approaches in order to demonstrate the performance differences and similarities between these two types of approaches in the presence of partial occlusions. Various experiments are carried out on CASIA, UPOL and UBIRIS databases to demonstrate the effect of occlusions on iris recognition with holistic and subpattern-based approaches.