Discriminant iris feature and support vector machines for IRIS recognition

In an iris recognition system, the size of the feature set is normally large. As dimensionality reduction is an important problem in pattern recognition, it is necessary to reduce the dimensionality of the feature space for efficient iris recognition. In this paper. we present one of the major discriminative learning methods, namely, Direct Linear Discriminant Analysis (DLDA). Also, we apply the multiresolution wavelet transform to extract the unique feature from the acquired iris image and to decrease the complexity of computation when using DLDA. The Support Vector Machines (SVM) approach for comparing the similarity between the similar and different irises can be assessed to have the feature's discriminative power. In the experiments, we have showed that that the proposed method for human iris gave a efficient way of representing iris patterns.