Biometric template classification: A case study in iris textures

Most of the biometric authentication systems store multiple templates per user to account for variations in biometric data. Therefore, these systems suffer from storage space and computation overheads. To overcome this problem the paper proposes techniques to automatically select prototype templates from iris textures. The paper has two phases: one is to find the feature vectors from iris textures that have less correlation and the second to calculate DU measure. Du measure is an effective measure of the similarity between two iris textures, because it takes into consideration three important perspectives: a) information, b) angle and e) energy. Also, gray level co occurrence matrix is used to find the homogeneity and correlation between the textures.