Performance Evaluation of Iris Based Recognition System Implementing PCA and ICA Encoding Techniques

In this paper, we describe and analyze the performance of two iris-encoding techniques. The first technique is based on Principle Component Analysis (PCA) encoding method while the second technique is a combination of Principal Component Analysis with Independent Component Analysis (ICA) following it. Both techniques are applied globally. PCA and ICA are two well known methods used to process a variety of data. Though PCA has been used as a preprocessing step that reduces dimensions for obtaining ICA components for iris, it has never been analyzed in depth as an individual encoding method. In practice PCA and ICA are known as methods that extract global and fine features, respectively. It is shown here that when PCA and ICA methods are used to encode iris images, one of the critical steps required to achieve a good performance is compensation for rotation effect. We further study the effect of varying the image resolution level on the performance of the two encoding methods. The major motivation for this study is the cases in practice where images of the same or different irises taken at different distances have to be compared. The performance of encoding techniques is analyzed using the CASIA dataset. The original images are non-ideal and thus require a sequence of preprocessing steps prior to application of encoding methods. We plot a series of Receiver Operating Characteristics (ROCs) to demonstrate various effects on the performance of the iris-based recognition system implementing PCA and ICA encoding techniques.