CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleBiometric Personal Identification
Thesis Advisor谭铁牛
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract本文作者在模式识别国家重点实验室攻读硕士学位期间,主要从事基于生物统 计特征身份鉴别算法的研究,具体包括笔迹鉴别、虹膜识别和字体识别。本文总结 了作者在这三个方面的工作。 第一章介绍了基于生物统计特征的身份鉴别技术的背景、有关概念和常用方法, 着重介绍了几种常用的生物统计特征,包括声音、红外温谱、脸像、指纹、虹膜、 笔迹和步态,并给出了各种生物特征之间的比较。 第二章从系统结构的角度介绍了基于生物特征的身份鉴别系统,并给出了多生 物特征融合系统的概念和应用。 第三章提出了一种与内容无关的离线笔迹鉴别的算法。该算法把笔迹作为包含 某种纹理的图像来看待,将笔迹鉴别的问题转化为纹理识别来处理。算法采用多通 道二维Gabor滤波器来提取这些纹理的特征,使用加权欧氏距离分类器来完成匹配 工作。在实验中,我们使用了17个人的不同笔迹,取得了较好的结果。 第四章提出了一种基于虹膜识别的身份鉴别算法。该算法由虹膜图像获取、虹 膜图像预处理、虹膜特征提取和分类器设计四个部分组成。算法采用纹理分析的方 法进行虹膜特征提取,与现有算法相比,本算法利用了虹膜所具有的丰富的二维纹 理特征,并且具有平移、旋转和缩放的不变性。 第五章提出了一种基于纹理分析的字体识别的新算法。该算法把包含某种字体 的文档看作具有特定纹理的图像,把字体识别的问题作为纹理识别来处理。这种方 法提取和分析全局纹理特征,它是一种与内容无关的方法。我们做了大量的实验, 对于常用的24种中文字体(6种字型,4种风格)和32种英文字体(8种字型,4 种风格)一共进行了14,000个样本的实验,取得了非常好的识别结果。
Other AbstractFor the past two years at the National Laboratory of Pattern Recognition, the author has been working on biometric personal identification. His work includes writer identification, iris recognition and font recognition as described in this Master thesis. The thesis is organized as follows. Chapter 1 gives an introduction of biometric personal identification and an overview of the biometric technology. A number of biometrics-based technologies are briefly introduced and their comparisons are presented. Chapter 2 deals with the structure of a biometrics-based personal identification system. It also addresses the concept and application of multi-biometric personal identification systems. Chapter 3 describes a content independent algorithm for off-line writer identification. The new algorithm takes the handwriting image as an image containing some special texture, and regards writer identification as texture identification. We apply the well-established 2-D Gabor filtering technique to extract features of such textures and a weighted Euclidean distance classifier to fulfill the identification task. Experiments are made using Chinese handwritings from 17 different people and very promising results were achieved. Chapter 4 presents a new algorithm for personal identification based on iris patterns. It is composed of iris image acquisition, image preprocessing, feature extraction and classifier design. The algorithm for iris feature extraction is based on texture analysis using multi-channel Gabor filtering and wavelet transform. Compared with existing methods, our method employs the rich 2-D information of the iris and is translation, rotation, and scale invariant. Chapter 5 describes a new texture analysis based approach towards font recognition. In this new method, we take the document as an image containing some special textures, and font recognition as texture identification. The method is content independent and involves no local feature analysis. Experiments are made using 14,000 samples of 24 frequently used Chinese fonts (6 typefaces combined with 4 styles) as well as 32 frequently used English fonts (8 typefaces combined with 4 styles). Very promising results are obtained.
Other Identifier562
Document Type学位论文
Recommended Citation
GB/T 7714
朱勇. 基于生物特征的身份鉴别[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2000.
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