Multi-source Heterogeneous Iris Recognition Using Locality Preserving Projection

Abstract

Multi-source heterogeneous iris recognition (MSH-IR) has become one of the most challenging hot issues. Iris recognition is too dependent on the acquisition device, causing have large intra-class variations, capture iris duplicate data more and more larger. The paper proposed the application of locality preserving projection (LPP) algorithm based on manifold learning as a framework for MSH-IR. Looking for similar internal structures of iris texture, MSH-IR is performed by measuring similarity. The new solution innovation aspects that LPP algorithm is used to establish the neighboring structure of the similar feature points of the iris texture, and the similarity between the MSH-IR structures is measured after mapping to the low-dimensional space, and using the SVM algorithm to find and establish the optimal classification hyperplane in low-dimensional space to implement the classification of multi-source heterogeneous iris images. The experiment based on the JLU-MultiDev iris database. The experimental results demonstrates the effectiveness of the LPP dimension reduction algorithm for MSH-IR.