Research On Iris Image Localization And Recognition Algorithm

With the continuous development of the internet and information technology,people are more concerned about their privacy and the security of personal information.Traditional authentication methods have been difficult to meet people's requirements.Under such circumstances,biometrics technology is becoming more and more popular because of its convenience and security.Among them,iris recognition technology has become a research hotspot in the field of biometrics due to its high security and high stability.In order to improve the system recognition performance,several key technologies in iris recognition were studied and improved,including iris location,feature extraction and recognition.The main content of this article includes the following aspects:(1)The unique physiological characteristics of the iris were analyzed.Each key technology in the iris recognition system was introduced in detail,and the performance evaluation indicators of the recognition system were introduced.(2)Localization algorithms of iris images were studied.Firstly,based on the grayscale characteristics and near-circle characteristics of the pupil,an algorithm for iris edge location based on the combination of adaptive threshold method and least square method was proposed.Then,on the basis of the edge characteristics of the pupil center and the outer edge,an iris outer edge localization method based on the extended ray method was proposed.Based on this,the calculus operator method was further improved and the search area was reduced to improve the efficiency of iris localization.(3)Iris feature extraction and recognition algorithms were studied.Firstly,in order to give full play to the feature extraction performance of the Gabor filter,the parameters of the filter were analyzed and researched.Then,a method based on block iris feature extraction was proposed,and the Gabor filter was adaptively optimized by particle swarm optimization to effectively improve the algorithm's adaptability to different iris databases.Finally,the nonlinear search capabilities of support vector machines were used to efficiently match and identify iris features.(4)The CASIA iris library of the Chinese Academy of Sciences was used to analyze and verify the proposed method.Firstly,the effects of parameters in the proposed feature extraction method were analyzed by orthogonal experiments,and the optimal parameter combination was determined.Then,the algorithm of positioning algorithm and recognition algorithm were tested respectively.The accuracy of positioning was 98.81%,and the recognition rate was 99.23%.Compared with the traditional method,the positioning and recognition rates were increased by 1.32% and 0.59%,respectively.The results show that the proposed method can quickly and effectively locate the iris and significantly improve the recognition performance of the system.