Robust ear based authentication using Local Principal Independent Components

This paper presents the ear based authentication using Local Principal Independent Components (LPIC) an extension of PCA. As PCA is a global approach dealing with all pixel intensities, it is difficult to get finer details from the ear image. The concept of information sets is introduced in this paper so as to have leverage over the local information. These sets are based on the granularization of the ear image in the form of windows. The features based on these sets allow us to change the local information which goes into LPIC as the input. Thus LPIC not only uses this local information but also helps to reduce the dimensions of the deduced features far less than that can be achieved with PCA. For the extraction of sparse information from ear, features such as Effective information (EI), Energy feature (EF), Sigmoid feature (SF), Multi Quadratic feature (MQD) are derived and then LPIC is applied to get the reduced number of features. Inner product classifier (IPC) is developed for the classification of these features. The experiments carried out on constrained and unconstrained databases show that LPIC is effective not only under the ideal conditions but also under the unconstrained environment.