Representação e classificação de texturas da íris baseado na análise discriminante de Fisher bi-dimensional

Abstract

The recent advances of information technology and the growing security requirements have led to the fast development of intelligent person authentification techniques based on biometric recognition. In this work iris images, from UBIRIS data base, are applied as biometric measurements in the verification scenery. The literature shows a large variety of feature extraction methods applied to the iris images recognition process. In this work we apply two methods based on subspace. The aim of subspace methods is to find feature vectors which reduces the space dimension while also optimizes the class separation. Some of the most known subspace methods are Principal Components Analysis (PCA), Liner Discriminant Analysis (LDA), and Independent Component Analysis (ICA). In this work we employ two extensions of FDA, i.e., D FDA 2 (2 ) and DiaLDA+2FDA for feature extraction. During the classification phase it was applied the nearest neighbor classifier with the Euclidean distance. The results showed that the methods have a good performance, with emphasis on dimension reduction. The methods compress a 200 92 matrix dimension in a 5 5matrix, with an AUC of 0.99.