IRIS based authentication using local principal independent components

The Local Principal Independent Components (LPIC) developed as an extension to the Principal Component Analysis (PCA) based on the information set are utilized for the iris based authentication. LPIC allows the extraction of not only the local texture information present in the Iris image but also reduces the dimension far less than that can be achieved with PCA. Four types of information set features such as Effective information (EI), Energy feature (EF), Sigmoid feature (SF), Hanman transform (HT) are formulated and the corresponding LPIC features much less in number are derived and then the test set is classified with the Hanman classifier. The experiments carried out on CASIA-Iris-Lamp database show that LPIC outperforms PCA using the Hanman Classifier mostly. The proposed approach gives 99.2% whereas PCA gives 27.7% with only 30% of features.