生物特征识别论文：生物特征识别 虹膜识别 小波变换 粗集理论

生物特征识别论文：生物特征识别 虹膜识别 小波变换 粗集理论 生物特征识别论文:生物特征识别 虹膜识别 小波变换 粗集理论 【中文摘要】随着现代社会的高度信息化,人们对身份识别准确性和可靠性的需求也变的更高,基于生物特征识别的身份鉴别方法得到了广泛的应用。虹膜识别技术与人脸、指纹等生物识别技术相比,具有更高的可靠性、稳定性和非侵犯性,被认为是最有前景和最为安全的,这使它成为近年来生物识别领域的研究热点。但是,对于虹膜识别技术的大规模使用仍然存在着一些技术上的问题,使得虹膜识别成为模式识别和机器视觉中最富挑战性的课题之一虹膜识别系统一般由三部分组成:虹膜图像的获取、图像的预处理(虹膜定位、归一化)、特征提取和模式匹配(认证识别)。本文在系统地 分析 定性数据统计分析pdf销售业绩分析模板建筑结构震害分析销售进度分析表京东商城竞争战略分析 和整理了前人的研究成果的基础上,就虹膜图像预处理、特征提取与模式匹配等方面对虹膜识别技术的算法做了深入研究和改进。本文主要的研究工作包括以下几方面:(1)在虹膜图像定位上提出了一种更为快速和精确的定位算法。采用计算候选圆心与粗定位边缘点的最小方差和对候选点逐点检测的方法实现精确定位。仿真结果 表 关于同志近三年现实表现材料材料类招标技术评分表图表与交易pdf视力表打印pdf用图表说话 pdf 明,本算法思路简单,易于实现,在保证速度的基础上有效提高了定位的精度,避免了搜索的盲目性。(2)在特征提取上引进了粗集理论的概念,利用粗集的粗分类思想和数据离散化方法对特征进行分析处理,对虹膜实现粗分类。首先应用三层小波分解对归化后的虹膜图像进行特征提取,小波变换良好的时频分析特性可以有效的提取出表征虹膜纹理特征的信息,这些信息经过Kohonen网络离散化后生成满足粗集处理要求的数据表,从而 实现对虹膜的粗分类,避免了后续匹配过程的盲目性,节省了识别时 间。(3)在编码匹配方面,为了改善小波变换不能很好的表征纹理信息 局部特性的缺陷,我们首先选取小波分解后包含最丰富信息特征的第 三层分解系数,进行量化编码,然后对归一化图像进行子块划分,分别 对每一子块进行三层小波分解,取其第三层的中频分量量化编码,与 第三层分解系数的编码共同组成用于模式匹配的编码模板。然后对编 码模板应用Hamming距离进行相似度计算,进而实现识别过程。仿真 结果表明,本文基于小波变换和粗集理论的虹膜识别方法与传统的方 法相比,在识别精度上有了显著提高。小波变换在时—频域分析中所 具备的优良特性使其成为信号处理的一种强有力的工具。粗集在处理 不确定性问题上的优良性能提高了系统的鲁棒性和抗干扰性。本文论 述的虹膜识别算法在matlab7.0的环境下,利用CASIA虹膜数据库 (version 1.0)中的图像样本进行仿真测试,得到了良好的识别效果。 【英文摘要】Along with the development of modern informationalized society, the requirement of more accurate and reliable on the identification becomes higher, and identification methods based on biometric identity have been widely used. Compared with face, fingerprint and other biological characteristics, iris identification, which has higher reliability, stability and non-invasive, is considered the most prospects and safe technology and has become a research focus in the biology identification field. But, iris identification still exists some technical problems in large-scale use, which makes iris identification become one of the most challenging issues in pattern recognition.Iris identification system usually consists of three parts:iris image acquisition, image preprocessing (iris location, normalized), feature extraction and pattern matching (the certification and recognition). Systematic analysis and sorting the previous studies, this paper makes deeply research and improvement of iris recognition algorithm. The main work of this paper includes the following respects:(1)In the iris positioning, this paper puts forward a more rapid localization algorithm. For the inside localization, firstly, roughly locates the circle point and radius of the pupil used the binary and gray-level projection methods. Then calculates the minimum variance of the candidate circles and the rough edge points accurately to obtain the parameters of inside edge. For the case of multiple minimum variances, uses point-by-point detection to ask out the most accurate parameters of inside edge. For the outside localization, firstly detects outside edge used the canny operator, and then crops and denoises the iris image based on the experience values. Then determines the outside edge through the minimum variance between the candidate points and the boundary points. Finally, asks out the most relatively accurate boundary parameters used point-by-point detection. Compared with the traditional algorithm, this algorithm is simple, easy to realize, improves the positioning accuracy and avoids the blinding search so as to improve the accuracy of the overall iris identification. Experimental results verify the feasibility of the proposed algorithm.(2)In feature extraction, this paper introduces the concepts of rough sets theory. Using the coarse screening thought and data discretization method of rough sets to roughly classify iris images. Firstly, extracts iris characteristics though applying 3 layers wavelet decomposition on the normalized image. Then, through Kohonen network discretization generates the data tables meeting the requirements of rough sets, which can roughly classify the iris and avoid the blindness of the subsequent matching process and save the recognition time.(3)In encoding and matching, in order to improve the defects that wavelet transform can’t sign local characteristics texture very well, this paper firstly selects the third layer decomposition coefficients of wavelet which contains the most abundant information and then encodes quantitatively. Next divides the normalized image into sub-blocks. For each sub-block, applies wavelet decomposition and takes the third intermediate frequency components to encode quantitatively. These codes together with the codes of the third layer decomposition coefficients composed of the coding template for pattern matching. Finally, calculate the templates’similarity through the hamming distance to recognize the iris.Experimental results show that compared with the traditional methods, the wavelet transform combineds with rough sets theory has significantly increased the accuracy of the iris recognition. Wavelet transform, which has excellent properties in time and frequency domains has been a powerful tool in signal processing and can not be replaceable. Rough sets, which has excellent properties in dealing with uncertainty questions can effectively improve the system’s anti-interference performance.The iris identification algorithm this paper proposed obtains well recognition results in the matlab7.0 environment, simulating the test samples of CASIA iris database (version1.0). 【关键词】生物特征识别 虹膜识别 小波变换 粗集理论 【英文关键词】biometric identification iris recognition wavelet transform rough sets hamming distance 【目录】小波变换和粗集理论相结合的虹膜识别算法研究 摘 要 8-10 Abstract 10-11 第1章 绪论 12-20 1.1 人体生物识别技术 12-15 1.2 虹膜识别的研究意义 15-16 1.3 虹膜识别的应用 16-17 1.4 本论文的主要内容 17-20 第2章 虹膜识别系统及算法 20-28 2.1 虹膜的生理结构 20-21 2.2 虹膜识别系统 21-22 2.3 虹膜识别的主流算法 22-26 2.3.1 虹膜定位算法 22-24 2.3.2 特征提取算法 24-26 2.4 本文采用的虹膜图像数据库 26-27 2.5 本章 小结 学校三防设施建设情况幼儿园教研工作小结高血压知识讲座小结防范电信网络诈骗宣传幼儿园师德小结 27-28 第3章 虹膜图像预处理 28-42 3.1 虹膜的定位 28-36 3.1.1 内边缘定位 28-33 3.1.2 外边缘定位 33-36 3.2 虹膜图像归一化 36-37 3.3 图像增强 37-38 3.4 虹膜图像的噪声处理 38-41 3.4.1 光斑的处理 38-39 3.4.2 眼睑的检测 39-40 3.4.3 睫毛的处理 40-41 3.5 本章小结 41-42 第4章 基于小波变换和粗集理论的虹膜图像特征提取和编码 42-58 4.1 基于小波变换的特征提取 42-49 4.1.1 小波变换的基本概念 42-44 4.1.2 小波基 函数 excel方差函数excelsd函数已知函数     2 f x m x mx m      2 1 4 2拉格朗日函数pdf函数公式下载 的选取 44-45 4.1.3 小波变换提取特征值 45-49 4.2 基于粗集理论的虹膜粗分类 49-55 4.2.1 粗集理论的基本概念 49-52 4.2.2 基于粗集理论的虹膜样本粗分类 52-55 4.3 虹膜的特征编码 55-57 4.4 本章小结 57-58 第5章 模式匹配与结果分析 58-64 5.1 图像的模式匹配 58-60 5.1.1 匹配方式分类器 58-59 5.1.2 基于Hamming距离的模式匹配 59-60 5.2 实验仿真与结果分析 60-63 5.2.1 认证模式 61-62 5.2.2 识别模式 62-63 5.3 本章小结 63-64 第6章 总结与展望 64-66 6.1 工作总结 关于社区教育工作总结关于年中工作总结关于校园安全工作总结关于校园安全工作总结关于意识形态工作总结 64-65 6.2 研究展望 65-66 参考文献 66-70 致谢 70-72 作者攻读硕士学位期间发表的论文 72-73 学位论文评阅及答辩情况表 73 【采买全文】 1.3.9.9.38.8.4.8 1.3.8.1.13.7.2.1 同时提供论文写作一对一辅导和论文发表服务.保过包发 【说明】本文仅为中国学术文献总库合作提供，无涉版权。作者如有异议请与总库或学校联系。