A study on multi-unit iris recognition

Iris recognition system has achieved good performance, but it is affected by the quality of input data. In this paper, we propose a multi-unit iris recognition system, which can select the good quality data between multi-unit eye images of the same person. The system is composed of four stages. First, both iris data are captured at the same time. After that the eye image check algorithm rejects noisy and counterfeit data. At the third stage, features are extracted by Daubechies´ wavelet. Finally, features are classified by support vector machines (SVM) and Euclidian distance. We select the better accuracy rate between results of two methods. Experiment results involve 1694 eye images of 111 different people and the best accuracy rate is 99.1%.