【作者】 来毅；

【导师】 卢朝阳；

【作者基本信息】 西安电子科技大学 ， 通信与信息系统， 2007， 硕士

【摘要】 虹膜识别因其具有高可靠性、稳定性和非侵犯性,已成为生物特征识别领域的一个研究热点。眼睑和眼睫毛遮挡是虹膜识别系统中较难检测的两种噪声,对系统识别性能影响较大,本文对如何去除这两种噪声进行了较深入的研究。文中介绍了虹膜身份识别系统各个关键部分,分析了现有眼睑和眼睫毛遮挡检测算法的优缺点,提出了基于灰度形态学的眼睑和眼睫毛遮挡检测算法:一是设计弧线形的形态学结构元素,经过灰度开启运算、图像分割和边缘检测,获得眼睑边缘的候选点集,再利用Bezier曲线拟合出眼睑边缘;二是构造交叉形的形态学结构元素,通过灰度开启运算得到直方图具有分段特性的虹膜图像,经二值化检测出眼睫毛。实验结果表明:该算法能有效地检测两种遮挡噪声,有助于降低虹膜识别系统的等错误率,提高模式的可分性。另外,本文还对虹膜识别算法进行了初步探讨。在分析2-D复Gabor滤波器实部和虚部的幅频响应特性后,发现2-D奇Gabor滤波器比2-D复Gabor滤波器更能有效提取虹膜纹理特征。因此,提出采用2-D奇Gabor滤波的方法编码虹膜纹理特征点的相位信息,并通过计算汉明距离(Hamming distance)进行识别。实验结果表明,与基于2-D复Gabor滤波编码虹膜纹理相位特征的方法相比,该算法使系统识别等错误率(Equal Error Rate)降低大约12%。更多还原

【Abstract】 Iris recognition has been becoming an active topic in biometrics due to its high reliability for personal identification. The occlusions of eyelid and eyelash in iris image are two kinds of noise to be difficult to detect and lower the performance of the system. In this dissertation, detections of these two kinds of noise are studied. Each crucial components of iris recognition system is described. Detailed analysis is given about advantages and disadvantages of the current occlusion detecting algorithms. Two gray-scale morphological noise detection algorithms are presented for the eyelid and eyelash occlusions, respectively. Firstly, an arc morphological structuring element is designed for detecting eyelid edge. A set of candidate points for eyelid edge can be obtained by gray-scale morphological opening, image segmenting, and edge detecting. Then the eyelid edge is fitted on the basis of Bezier curves. Secondly, a crossed morphological structuring element is developed. An iris image, whose intensity mostly distributes in several sections, can be acquired after gray-scale morphological opening. Thus a binary image of eyelashes is obtained. The experimental results indicate that the proposed algorithms have better effectiveness on detecting these two kinds of occlusion noises, and are helpful to decrease the Equal Error Rate of iris recognition system and improve the discriminability of iris patterns.In addition, an algorithm for iris recognition is also discussed. After analyzing the amplitude-frequency responses of real and imaginary part of the 2-D complex Gabor filter, we find that the 2-D odd Gabor filters is more effective than 2-D complex Gabor filter in extracting the features of iris texture. Then an algorithm for encoding phase information of iris texture with 2-D odd Gabor filters is introduced. Pattern matching is realized by computing Hamming distance between two templates. The experimental results show that the Equal Error Rate (EER) of system by using this method has about 12% decrease compared with the method for encoding phase information of iris texture based on 2-D complex Gabor filters.更多还原