An Effective Iris Recognition Method Based on Scale Invariant Feature Transformation

The parameter selection of SIFT operator is the premise and difficulty of feature extraction with SIFT. Based on \(a\!\!\!\!\!\!=\) analysis of the change regulation between each parameter of SIFT operator and the valid key points in detail, a variety of parameter selection ways to fit to extract iris texture features are put forward in this paper. A new set of feature matching method is designed and realized according to the features. According to the experimental results from three public iris databases, including CASIA V1.0, CASIA-V3-Interval and MMU, compared with classical SIFT method of Lowe, the method we proposed has been proven to increase by 2% to 5% in recognition accuracy. It shows that the method we proposed has strong robustness and high recognition ability.