An Effective Iris Recognition Method Based on Scale Invariant Feature Transformation

摘要

The parameter selection of SIFT operator is the premise and difficulty of feature extraction with SIFT. Based on 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.