The Research And Implementation Based On Iris Identification Effect Evaluation System

Biological information recognition is one of the typical pattern recognition technology, which integrates biological and information technology. It is based on personal unique physiological and behavioral characteristics of automatic identification and validation. With the increasing of biometrics application scope and depth of the study, various identification algorithms are endless. But so far, most of the work mainly focus on the research of the core recognition algorithm for these algorithms, and there are few comparative studies to evaluate algorithm. Thus realizing biometric systems of linear flow optimization choice, is the main problems in front of the biological information identify fields. The above questions demand biological information recognition algorithm for evaluating the concept, looking for the effective evaluation method, to speed up the biological information identification technology and industrialization of mature, and make it a high-tech to serve the society.Biological information recognition in the international effect evaluation has got widely attention, but the present domestic work also lack of attention. Aiming at the weakness in this field by iris identification system, based on biological effect, evaluation, this paper makes a research on the job:(1) The first proposed mathematical modeling application in biological information identification algorithm assessment concept, study indicates route to iris identification system, quantitative representation in more effectively iris identification system on the basis of the condition, which is representative of the influence factors as parameters, and the quantized treatment, to evaluate the affine model, using the objective, effective evaluation model of quantitative evaluation results.(2) The comprehensive fuzzy evaluation model, fuzzy integral model, fuzzy clustering model are regarded as evaluating the mathematical model. This paper expounds the basic thoughts of three kinds of models and selection. The comprehensive fuzzy evaluation model is the comprehensive consideration of all factors, from the actual evaluation on the assessment result, The fuzzy integral model is very suitable for algorithm based on recognition. In the fuzzy clustering model, each sample not only belongs to a kind of, but in every class membership respectively. For the three basic principles of model derivation, this paper also makes a detailed description of the evaluation work and lay a solid foundation for model selection.(3) The evaluation model based on the iris recognition system is taken as the platform. In order to effectively complete assessment, this paper describes the working process of the iris identification, which is divided into 10 stages: image quality evaluation, image denoising, iris, iris image enhancement, iris normalization, feature extraction, features and characteristics of coding security encoding, decoding and feature matching. Using object-oriented method, this paper expounds the main functions of each stage, the main algorithms. Through the graph mode analyzes identification effect influence, and the identification of attribute data transfer between the phase relationship.(4) Based on mathematical model for each stage, iris identification and extraction of attribute properties to parameters in the model of affine method is introduced. Especially some which need complete qualitative to quantitative attributes of transformation are strictly defined. At the same time, this paper makes further research on the comprehensive fuzzy evaluation model and illustrates the multi-level fuzzy comprehensive evaluation model of the basic ideas and theories.(5) In the Windows operating platform based on computer systems, iris identification, assessment of the effect of software test platform is developed by VisualBasic6.0 to complete testing and analysis. Using the lab self-developed IRIS images acquisition device JLUBR to establish IRIS images - IRIS is selected as the experimental IRIS images. Selection of image quality evaluation stage respectively the airspace frequency-domain algorithm combining evaluation and quality evaluation algorithm, iris sequence image denoising phase of wavelet denoising algorithm, iris stage from coarse to fine step-by-step localization algorithm, the Snake localization algorithm, iris images enhance phase of local histogram algorithm, normalized stage double polar affine transformation algorithm, feature extraction phase of wavelet had zero algorithm and wavelet multi-scale characteristic feature matching algorithm and quantification of Svm stage of support vector machine (Svm) method for testing. Fuzzy comprehensive evaluation model is applied to each phase of iris identification, iris identification algorithm according to the choice of each phase extraction algorithm for attribute values, relevant quantized treatment, then the affine to the comprehensive fuzzy evaluation model, according to each parameter identification of effect factors determine the weight, definite extent and comments, the corresponding variation of each phase of the selected algorithm, the comprehensive evaluation of the level of each level assessment conclusion results as secondary fuzzy comprehensive evaluation factors, also need to identify weight, clear and comments, and finally using secondary fuzzy comprehensive evaluation this paper makes that assessment conclusion of the variation of iris identification process.Using the fuzzy integral model to assess feature extraction phase of wavelet modulus maximum algorithm, the wavelet had zero algorithm and wavelet multi-scale characteristic quantitative assessment, obtains algorithm for each of the algorithm. Conclusion: through comparison for these laboratory experiment of samples, the wavelet multi-scale characteristic quantitative algorithm is best.Using fuzzy clustering model of feature extraction phase of wavelet multi-scale features of wavelet and quantitative algorithm have zero setting, according to evaluate algorithm of wavelet threshold, multi-scale features of quantitative evaluation result, the algorithm of wavelet and excellent after zero algorithm is relatively poor.The experiment and test results, usually is validated the effectiveness evaluation system validity and practicability. This paper designs the recognition evaluation system with complete functions, reasonable structure. It is easy to expand features, and has strong practical value.To sum up, this research has certain theoretical significance and application value for biological information identification, assessment of the effect of solving problems, and provides the beneficial ways and means.