On-line fingerprint verification
人员信息认证英语
人员信息认证英语一、单词1. Verify- 英语释义：to make sure that something is true, accurate, or justified.- 用法：verify + sth.（名词或名词短语），verify + that从句。
- 例句：Thepany will verify your identity before granting you access.（在给予你访问权限之前，公司将核实你的身份。
）2. Authenticate- 英语释义：to prove or show something to be true, genuine, or valid.- 用法：authenticate + sth.- 例句：You need to authenticate your account toplete the registration.（你需要认证你的账户以完成注册。
）3. Confirm- 英语释义：to state or show that something is definitely true or correct.- 用法：confirm + sth.，confirm + that从句。
- 例句：Please confirm your personal information.（请确认你的个人信息。
）4. Identity- 英语释义：who or what a person or thing is.- 用法：作名词，可用于短语“identity verification”（身份验证）等。
- 例句：Protecting your identity is very important in the digital age.（在数字时代，保护你的身份非常重要。
）5. Certificate- 英语释义：an official document proving that you havepleted a course of study or passed an exam, or that states the facts about something.- 用法：作名词，如“a birth certificate”（出生证明）；也可作动词，意为“发给结业证书，用证书证明”。
FingerPrint
Type of fingerprint
Fingerprint Feature Extraction and Numeric Meta-base Creation
1. Fingerprint Image Pre-processing The main steps involved in the pre-processing include: (a) enhancement (b) binarization (c) segmentation, (d) thinning. 2. Fingerprint Feature Extraction and Numeric Meta-base Creation
FingerPrint
History of using fingerprint as identification
1. Antiquity and the medieval period
Fingerprints have been found on ancient Babylonian clay tablets, seals, and pottery. They have also been found on the walls of Egyptian tombs and on Minoan, Greek, and Chinese pottery, as well as on bricks and tiles from ancient Babylon and Rome. Some of these fingerprints were deposited unintentionally by the potters and masons as a natural consequence of their work, and others were made in the process of adding decoration. However, on some pottery, fingerprints have been impressed so deeply into the clay that they were possibly intended to serve as an identifying mark by the maker.
海康威视产品说明书：无接触体温检测与戴口罩检测系统
Four adjustable lenses in one camera cover up to a 360° field of view, ensuring there are no monitoring blindspots. The monitoring tilt angle can also be adjusted.
Analysis
HikCentral
Trip & Fall Incidents
Loitering
Violent Motion
Guards check situation
Reception - Facial recognition & monitoring abnormal temperature.
NEED TO RECOGNIZE A VIP FROM YOUR BRANCH?
O ce & Customer Service Area - Indoor Panoramic Monitoring.
CAPTURE 360° IMAGES
MINI PANOVU CAMERAS
Hikvision’s PanoVu DS-2PT3326IZ-DE3 PanoVu Mini-Series Network PTZ camera, with integrated panoramic and PTZ cameras, is able to capture 360° images with its panoramic cameras, as well as detailed close-up images with the PTZ camera.
cheating on exams
考试作弊cheating on examsHigh-tech measures, such as face recognition and fingerprint verification systems, will be used to fight cheating for the first time in many places for this year's gaokao, or national college entrance exam.今年高考期间，多地将首次使用面部识别和指纹识别等高科技手段防止作弊行为。
考试作弊（cheating on exams）一直是所有学校和老师头疼的问题，在关系到考生命运的高考期间，这个问题更是需要多方重视。
近日，教育部提出的防替考措施包括：采用二代身份证现场报名确认（identity verification on the site）、现场采集照片和指纹或指静脉等生物特征（photo taking, fingerprint reading and vein scanning）、及时进行信息比对（timely data match）等。
静脉验证（finger vein verification）是一种新的生物特征识别技术（biometric identification technology），它利用手指内的静脉分布图像（finger vein pattern）来进行身份识别。
与指纹识别在程序上大致相同，但指纹容易被人复制，指静脉目前被人复制的可能性基本为零。
去年11月1日，《刑法修正案（九）》实施，明确组织作弊（organize cheating）、提供作弊器材（provide equipment or help for cheating）、非法出售或提供试题答案（illegally sell exam questions and answers）、代考替考（take tests for somebody else）等4类行为最高可判处七年有期徒刑（a sentence of up to seven years imprisonment）。
4指纹指纹Fingerprint...
摘要随着电子信息化浪潮的到来传统的身份鉴定方式密码于是生物识别技术应运而出如步态提出并实现了一套基于此两种生物特征的身份验证系统发展历史和应用针对本文利用到的指纹识别和掌型验证两种技术预处理增强并结合本文的设计目的提出了适合本系统的各种技术方案融合指纹和掌型的身份验证系统分成指纹验证和掌型验证两个子系统对算法模块进行介绍基于方向场在二值化以及最后利用细节点构造Delaunay三角形对指纹图像进行比对验证的算法并提取其特征点当然以提高系统的安全级别与目前文献上其他的类似算法进行了比较提出了将来工作的方向身份验证掌型验证AbstractAccurate personal identification is becoming more and more important to the operation of our ever increasingly electronically inter-connected information society. Traditional personal identification methods, such as: key, password, ID card, definitely can not satisfy the increasing security requirements. Due to its uniqueness and stability, biometric technique has become the most promising candidate. In this thesis, we focus on fingerprint and hand geometry verification, these two kinds of biometric techniques, and propose a bimodal biometric verification system based on these two biometrical characteristics.The origin and developing history of biometrics are introduced firstly, then we conclude and compare the advantages and deficiencies of all biometric characteristics, also the application area of each biometric technique is given. The popular topic of current biometrics research—multimodal biometric system is emphasized. Because the fingerprint and hand geometry are utilized in this thesis, a comprehensive literature investigation is done on these two topics. All the key techniques, like: fingerprint image segmentation, fingerprint enhancement, minutia extraction, fingerprint matching, hand salient points extraction and matching, are classified into several classes. According to the requirements of our verification system, the appropriate techniques are picked out. In chapter 3, a bimodal biometric verification system based on fingerprint and hand geometry is specified. After clearing the diagram of the verification system, we divide the whole system into two parts---fingerprint subsystem and hand geometry subsystem, to describe the model algorithm designation. In fingerprint verification subsystem, a segmentation algorithm based Canny edge detector is introduced first, then Gabor filter is applied to enhance the fingerprint image. Following the traditional way of extracting minutia, we use thresholds and morphological thinning operators to get the skeleton of fingerprint image. Here, with the help of tracking fingerprint ridges in thinned fingerprint images, those spurious minutiae connected with ridges are eliminated; later, ridge breaks are repaired according to both foreground and background characteristics. Finally, fingerprint matching is conducted on the Delaunay triangulation, which is constructed on the filtered minutia. For hand geometry verification subsystem, we explain that how toconvert an enrolled hand image into a black-and-white image, and how to extract salient points from the hand image. Finally, distance matrix matching is applied to compare two hand vector features. Of course, as a bimodal system, the strategy of fusing the two matching scores of fingerprint and hand geometry to make the final decision is described at the end of this thesis.We compare our experiment results with others’ in the existing literature to confirm the advantages of our algorithm, also try to analyze the deficiencies of the system. Finally, we conclude the whole thesis and give out the future plan.Keyword: Personal Verification, Biometrics Identification, Fingerprint Verification, Hand Geometry Verification, Feature Extraction, Feature Matching独创性声明本人声明所呈交的学位论文是我个人在导师指导下进行的研究工作及取得的研究成果本论文不包含任何其他个人或集体已经发表或撰写过的研究成果本人完全意识到本声明的法律结果由本人承担日期使用学位论文的规定允许论文被查阅和借阅可以采用影印在年解密后适用本授权书请在以上方框内打学位论文作者签名年月日日期1 绪论身份识别是人们日常生活现代社会对于人类自身身份识别的准确性身份标识物品有钥匙身份标识知识包括用户名往往将这两者结合起来首先是使用方便其次是经过较长时间的实际应用后ID卡等在人们心中具有较高的可信度一旦他人获得标识物品电子银行等的日益普及传统的身份鉴定方式无法满足当前信息化时代的要求于是1.1 生物识别技术的由来生物识别而是人类本身所固有的生理和或行为特征与传统的暗语不易伪造的特点典型的个体生理特征有掌纹脸部特征典型的个体行为特征指通过学习后天获得的特征步态等[1]ÒѾ-ÒýÆðÁ˹ú¼ÊѧÊõ½çȨÍþ»ú¹¹¹À¼ÆδÀ´ÈËÃÇÔÚÍøÉϹºÎï»ò½»Ò×ʱ都需要先在生物识别仪上进行身份认证逐渐自成系统有望在10年内达到每年20亿美元的规模[1]²¢²¿ÊðÁËÏà¹ØÑо¿ÏîÄ¿µ«Ñо¿²½·¥´óÌåÉϸú¹ú¼Êͬ²½ÕÆÎÆ1.2生物识别特征的要求及分类任何人体的生理和行为特征都可以用作生物识别的依据1独特性3易采集性符合以上要求的还是不够的性能速度接受程度抗伪装和抗攻击性而且不会对使用该系统的用户造成生理伤害掌纹人脸视网膜等等敲击键盘的频率即指利用个体的某一生理特征或某一行为特征来进行身份验证图1-1 生物鉴定系统框图由图1-1可知登记单元验证单元Enrollment²¢¸ù¾ÝÐèÒª±£´æÔÚÓ²ÅÌÉÏ»òÕß´Å¿¨ÉϵÈVerification³éÈ¡ÌØÕ÷Êý¾Ý这两个单元对时间的要求不同对处理时间要求不高On-Line1识别VerificationÒ»¶ÔÒ»ÈçÍøÂç½ÓÈëÊ×ÏÈÊäÈëÕʺÅIC卡等而传统的系统中这样生物特征取代了传统的口令和密码某人是否为其宣称的那个人Identification×îºóϵͳ¼ø¶¨³öÊäÈëµÄÌØÕ÷¶ÔÓ¦µÄÉí·ÝÒ»¶Ô¶à´ËÈËÊÇË-Á½ÖÖģʽϵÄϵͳ½á¹¹²»¾¡Ïàͬµ«ÆäÀëÏß²¿·Ö¶Ôʱ¼äÒªÇó±Èʶ±ðϵͳ¸ß±£´æ×ÅÖÚ¶àµÄÌØÕ÷Êý¾Ý¾¯Îñ˾·¨¹¤×÷ÖÐʹÓõÄÉúÎï¼ø¶¨ÏµÍ³¼´Îª´ËÀàϵͳµÄÖ÷ÒªÓ¦ÓÃʵÀý¼´ÈÚºÏÕÆÐͺÍÖ¸ÎƵÄÉí·ÝÑé֤ϵͳ答而不需要在较大的模板库中去搜索来回答这样系统的工作量相对较小也是众多厂商的主要研究对象是的答案是设定不同的门限或者对于每一个测试者真实用户被接收真实用户被拒绝假冒者被接收假冒者被拒绝2和3是错误输出错误拒绝率False Acceptance Rate而FRR 也被称作FNMR¼´FRR和FARFRR和FAR是相关的一对量反之较低的FAR对应较高的FRR则表明系统不会将假冒者鉴定为真实用户由于本文研究的是验证系统除了准确性之外对于验证系统特征提取和比对和比对时间对于识别系统输入特征同大量的保存在数据库中的模板相比对现在它开始在民众生活中广泛应用应用程序的登陆应用生物鉴定技术虹膜门禁系统等等更是生物鉴定技术的用武之地小区门禁系统都已经引入了生物鉴定技术美国的宾夕法尼亚州以防止冒领事件的发生1.6 各种生物识别技术的比较用来鉴定身份的生物特征可以是生理特征也可以是行为特征一般而言生理特征是先天的而且在人的一生中可能发生变化更具有独特性目前使用中的或者在研发之中的生物特征技术有九种指纹虹膜语音指纹视网膜签名和语音是行为特征图1-2 列出6种常见的生物特征(a) (b) (c)(d) (e) (f)图1-2常见的生物特征a指纹虹膜ｅ语音1.6.1人脸人脸(Face)是人们在日常生活中辨别亲属识别技术基于这些唯一的特征时是非常复杂的在面部被捕捉之后眼睛一个算法和一个神经网络系统加上一个转化机制就可将一幅面部图像变成数字信号面部识别是非接触的易于为大众接受使用者面部的位置与周围的光环境都可能影响系统的精确性面部识别技术的改进依赖于提取特征与比对技术的提高对于因人体面部的如头发机器学习功能必须不断地将以前得到的图像和现在的得到的进行比对同时很难进一步降低人脸识别技术的成本Iris½á¹¹ÖåÎƺÍÌõÎƵÈÌØÕ÷µÄ½á¹¹¶øÇÒºçĤÖÕÉú²»±ä×ȫµÄδ¾-¹ý´óÁ¿µÄ²âÊÔûÓÐʵÑéÖ¤Ã÷ɨÃèºçĤ²»»áÓ°ÏìÈËÌ彡¿µµÈµÈÒ²ÊÇÈËÑÛ¾ßÓеÄΨһÌØÕ÷Ò²±»³ÆΪV oice-print或者Speech-print[4]ÕâÖÖ¼¼ÊõÒ²Ò×ÓÚΪ´óÖÚËù½ÓÊÕ½¡¿µµÄÓ°ÏìÏÖÓм¼ÊõÔÝʱ²»ÄÜ׼ȷµÄ¼ø±ðÉùÒô1.6.4指纹指纹指纹形成于胎儿期现在已经基本了解了指纹的各项特性由于指纹鉴定的长期使用以及指纹鉴定的效果显著大众还难以接收指纹鉴定的民用需要新的算法以降低复杂度而且包括手掌皮肤上的曲肌纹和腕纹等尽管掌纹曲线长度尺寸及掌纹曲线之间的间距会随年龄的增大而变化只有在有局部明显的外伤或各种引起深层皮下组织溃坏的后天性疾病时不同个体的花纹即使相似曲肌纹和腕纹等的形态有一定的规律因此也可以作为生物特征进行身份识别并有商业产品流通包括手指的长度长度以及形状等因此十分适合带宽或者记忆容量受限制的系统进行使用目前研究表明掌型的唯一性不够因此掌型通常用来验证身份在一些机场已经开始了应用另外由于掌型特点的简单将其匹配分数进行融合将在第二章详细介绍掌型识别技术各种生物识别技术都有其自身的优缺点不能一概而论比如说一般认为指纹鉴定和虹膜鉴定在准确度上和速度上优于声音鉴定表1-1 各种生物鉴定技术比较[1]生物鉴定技术通用性独特性性能易采集性接受度抗假冒能力签名低低低高高低语音中等低低中等高低面部高低中等高高低面部温度谱高高低高高高虹膜高高高中等低高视网膜高高中等低低高掌型中等中等中等高中等中等手掌静脉图中等中等中等中等中等中等指纹中等高高中等中等高1.7 生物识别技术的融合从1.6节中比较的结果我们无法摒弃任何一种生物特征无疑将提高系统的准确率将人脸和指纹进行融合的系统将适用于老年人无法满足现有的指纹验证技术同时事实上, 将多种生物特征进行融合是当前生物识别技术发展的一个热门的发展方向那么那些信息可以融合在一起用来提高系统的性能呢１半导体采集仪½«¶àÖÖÉúÎïÌØÕ÷ÈÚºÏÔÚÒ»Æð½øÐÐʶ±ðͨ³£À´ËµÉãÏñÍ·¶ÔÈËÁ³ÅÄÕÕÊÇΪÁËÌá¸ßϵͳµÄʶ±ð¾«¶È±¾ÎÄËù½éÉܵÄÈÚºÏÖ¸ÎƺÍÕÆÐ͵Äϵͳ¾ÍÊôÓÚ´ËÀàµÄÈںϷ½Ê½3¶¼¿ÉÒÔ½áºÏÆðÀ´ÑéÖ¤¸ÃÓû§µÄÉí·ÝÕâÖÖÈںϷ½Ê½°üÀ¨µ½²»Í¬ÌØÕ÷ÌáÈ¡¼°Æ¥ÅäËã·¨µÄÈںϼ´ÔÚÑéÖ¤»òÕßʶ±ð¹ý³ÌÖÐÈç¶à½Ç¶ÈÈËÁ³Í¼ÏñµÄÆ´½ÓµÈ¸÷Óг¤´¦µ«ÊÇ×ÜÌåÉÏ˵À´ÒòΪÈں϶àÖÖÉúÎïÌØÕ÷µÄϵͳ²»½ö½öÄÜÌá¸ßϵͳµÄÑéÖ¤¾«¶È´Ó¸÷ÖÖÎÄÏ×Ò²¿ÉÒÔ¿´³öÌá³ö½«Ö¸ÎƺÍÕÆÐÍÈÚºÏÔÚͬһÑé֤ϵͳÖÐ图1-3 五种常见的融合多种生物识别技术的方式1.8 本文的结构本文提出了一种融合指纹和掌型的身份验证系统的设计方案将掌型与指纹结合起来对用户进行验证虽然目前已经有许多融合多种生物特征的系统存在多种生物识别技术的融合由于指纹验证的精确性和掌型匹配的快速简单互为补偿理的决策机制可以大大提高验证系统的准确性和验证速度在如下4个方面做了较为深入的研究: 指纹图像的分割及预处理本文也将结合这4个方面结构如下总体阐述了生物识别技术的由来第2章并分析指纹和掌型识别技术中的技术难点和发展方向系统设计详细描述基于指纹和掌型的身份验证系统的设计方法并给出各部分的实验结果实验结果及算法分析并且对国内外目前已有的类似系统进行比较第5章2 核心技术研究本文提出的身份验证系统利用到指纹和掌型这两种常见的生物特征尤其是在指纹识别方面的技术文献数不胜数使我们的工作能够一开始就基于前人的积累之上对这两种典型的模式识别技术进行分析和总结指纹识别技术是目前最成熟现在指的指纹识别技术是基于计算机的一种自动指纹识别系统AFISÈ´¶¼ÊDzÉÓõÄÈ˹¤Ö¸ÎÆר¼Ò·ÖÀà¼ø¶¨µÄ·½·¨¶¼·¢ÏÖ¹ýÓйØÖ¸ÎƵÄÎï¼þµ«ÊÇȴûÓÐÈκÎÕýʽµÄ¿ÆѧÎÄÏ×Äܹ»Ö¤Ã÷Õâ¸öÊÂʵGalton等人发起[5],[6]Edxard Henry 建立了著名的Henry System¸÷µØ½¨Á¢µÄÖ¸ÎÆ¿âѸËÙÅòÕÍÔÚ¼ÆËã»ú·¢Ã÷Ö®ºóFBI开始使用一种自动指纹识别的设备因此下面只针对自动指纹的识别技术做出介绍1) 指纹的采集指纹特征的提取指纹的匹配指纹的分类也是其中的一个模块而不将其作为系统的一个模块做详细介绍应用环境图2-1 自动指纹识别系统框图由图2-1可知依次是用户接口模块细节点数据库模块即IDÃŽûϵͳ¿ÉÌṩС¼üÅÌÈÃÓû§ÊäÈëÉí·Ý´úºÅ»òÕßÌṩ¶Á¿¨»úÌṩָÎƲɼ¯ÒǸøÓû§Â¼ÈëÖ¸ÎÆ»ò±»ÊÚȨÓû§(2)用户的指纹模板系统根据用户输入的标识数据库可以保存在系统中或者在外部存储媒介上可以保存在指纹锁内部的存储器上细节点数据库可以保存在硬盘上动柜员机系统的指纹验证系统如果每次用户使用ATM卡时可以考虑把指纹模板保存在用户的ATM卡上指纹模板的大小由系统的要求和匹配采用的算法决定验证效果越好指纹登记模块的任务是将用户的标识数据和指纹登记到系统数据库中提取出特征从图2.1可以看出之后经过质量评测子模块对指纹质量评价指纹有效面积大对于这些图像并标志出有效块指纹验证模块用于验证用户是否为真实用户或授权用户系统获取该指纹的数字图像产生输入指纹模板判定用户是否通过验证将进行图像增强针对上述每一模块的关键技术指纹特征提取并总结这4种核心技术的研究现状在法庭刑事应用中疑犯的手指先染上黑色的墨水也可以将印记的指纹转化为数字图像离线采集过程而现在几乎所有的民事和刑事的自动指纹识别系统均支持按照采集过程是否在线1和inkedfingerprintÖлñµÃµÄÖ¸ÎƵ«ÖÊÁ¿ÄÑÒÔ¿ØÖƸüÊÇ´óÖÚÎÞ·¨½ÓÊܵı£´æÖ¸ÎÆÖ±½ÓÇÖ·¸ÈËȨ˾·¨ÖÐÆðןܴó×÷ÓÃ活体采集指纹指直接从手指提取指纹而无需借助中间媒介这些方式的共同特点是将手指按压在传感器上当然这三种方式都有不同的特点大多数的光学录入设备是利用CCD将指纹图像转换成数字图像它经历了长时间实际应用的考验价格不高并能提供分辨率为500dpi的图像采集仪台板必须足够大才能获得质量较好的图像这种潜在指印降低了指纹图像的质量膜手指则代表另一个极而且体积小巧不过也由于体积较小超声波录入设备采取传送声波并通过手指它具有光学录入和硅芯片录入的优点其设备也比较昂贵表2-1将各种采集技术的性能做了比较表2-1 光学汗多的和稍脏干手指好的手指成像模糊属于硅芯片录入技术的采集仪３００2.1.3 指纹特征提取技术指纹是由死亡的手指表皮的角质层细胞组成的在一幅指纹图中见图2-2¿ÉÒÔ¶¨ÒåÕâÖÖÎÆÀí·½ÏòÐÅÏ¢×÷ΪָÎƵÄÈ«¾Ö 特征其中全局特征指的是指纹的奇异点而奇异点就是根据指纹的方向场计算出来的图2-2 脊线谷线示意图bb因为绝大多数的指纹细节点提取算法均属于此类方法有效的防止了噪声的干扰导致算法的计算量较大另一类方法是直接从指纹灰度图中提取细节点, 此方法于97年首次由Mario提出[8]È»ºó×ö³öÊÇ·ñ´ïµ½ÖÕÖ¹µã»òÕß·Ö²æµãµÄÅжÏÆä·½·¨ÊÇÀûÓÃÑØ׿¹Ï߸ú×Ù·½ÏòÉϼ¹Ï߻ҶȵÄÁ¬ÐøÐÔ¶ø¶¯Ì¬»ñµÃ¸ú×Ù²½³¤Æä·½·¨ÊÇÀûÓü¹Ï߸ú×Ù·¨ÏòÉÏÁ½±ß×î½üµÄÁ½¸ö¹ÈÏßλÖõĹØϵÀ´Åж¨ÖÕ½áµãÖ±½Ó»Ò¶È¸ú×ÙÌáÈ¡Ëã·¨²»ÓöÔȫͼ×öÂ˲¨µ«ÊÇÒ²ÈÝÒ×Êܵ½ÔëÉùµÄÓ°ÏìÌØÕ÷ÌáÈ¡µÄÄ¿µÄÊÇΪÁ˺óÐø²½ÖèÆ¥Åä×öÆ̵æÂ˲¨µÈ·½Ê½Æ¥Åä因此人们也提出了其他的指纹特征将指纹对不同频段的Gabor滤波器的结果作为指纹的特征进行匹配在[12]½«Ï¸½Úµã×÷ΪָÎƵľֲ¿ÌØÕ÷´«Í³µÄÌáȡϸ½ÚµãµÄËã·¨µÄ׼ȷ¶ÈºÍÊÊÓùã¶È¶¼ÓÅÓÚÖ±½Ó»Ò¶ÈÌáÈ¡µÄËã·¨Òò´Ë¿ª·¢³öÒ»Ì×ÊôÓÚ×Ô¼ºµÄÖ¸ÎÆ·Ö¸î2.1.4 指纹分类技术指纹的分类是根据指纹中心区域的脊线总体特征来进行的根据不同的总体特征arch图2-4列出了这五种指纹的例子因为如何将指纹识别技术应用到大规模的指纹库上是当前尚未解决的一个问题这样在指纹库中指纹的分类算法有基于句法因此没有对指纹的分类技术进行研究从而提高验证指纹的速度图2.4 不同类型的指纹而且成功的匹配来自同一手指的指纹将指纹的匹配方法分为两类细节点模型使用细节点来描述指纹特征X.D. Jiang等人使用的基于局部与整体结构的细节点匹配算法[17]½«Í¼ÏñÆ¥Åäת»¯ÎªÄ£Ê½Ê¶±ðµÄÎÆÀíÆ¥ÅäÎÊÌâ[13]ͨ¹ýµãģʽƥÅäµÄ·½Ê½±È½ÏÆäÏàËÆÐÔ¾ÜʶÂÊÕâÁ½ÏîÖ¸±êµÄ¸ßµÍÈçÄÜÁ¿×îС»¯½Ç¶È½áºÏÄ£ÄâÍË»ðµÄµãÆ¥ÅäµÈ[18][19][20]¿É¿¿¶È²»¹»µÈÎÊÌâ针对指纹匹配中的点模式匹配问题基于串距离2Ä¿Ç°´ó¶àÊýµÄÆ¥Åä·½°¸ÊôÓÚ¼¸ºÎÆ¥Å伸ºÎÆ¥ÅäÒ»°ãÀûÓü¸ºÎ¹ØϵÅжÏÁ½×éϸ½Úµã¼¯Î»ÖÃÌØÐÔµÄÏàËƳ̶ȴò·ÖÔ½¸ßÆ¥Åä·ÖÊý´óÓÚãÐÖµ·´Ö®Æ¥Åäʧ°ÜÊ×ÏÈͨ¹ýµü´ú¹ý³Ì¿ÉÒÔ´ïµ½½Ï´óµÄÆ¥ÅäÏàËƶȼÆËãÆ¥Åä¶ÈÐýת¼ÆËãÔڹ̶¨±ß¿òÖÐÆ¥ÅäµÄϸ½Úµã¶ÔÊý [21]´ËËã·¨½«Ö±½Ç×ø±êϵÖеÄÌØÕ÷ϸ½Úµã¼¯Õâ¸öËã·¨Ê×ÏÈÓÃ×îС¶þ³ËÄâºÏ·¨¹À¼Æ³öÁ½×éÌØÕ÷µã¼¯µÄÐýת½Ç¶ÈºÐУ׼µãλÖ÷ֱðÒÔij¸öУ׼µãΪÖÐÐÄת»»µ½¼«×ø±êÖн«½á¹û¹æ·¶»¯ÎªÆ¥Åä·ÖÊý[18]¿ÉÒÔ½«¾Ö²¿µÄϸ½Úµã¼¯¹¹³ÉÈý½ÇÐλòÕßÐÇ×´½á¹¹À´½øÐÐÆ¥Åä¶øÇÒÐèÒª±£´æµÄϸ½ÚµãÐÅÏ¢¼òµ¥ÇÒ׼ȷ¿É¿¿¼¯ÖÐÔÚ¶ÔÖ¸ÎƵÄÍØÆ˽á¹ûµÄÑо¿ÉÏÈçÅ×ÆúÁË´«Í³½¨Á¢µÄ¸ÕÐÔ×ø±êϵ½¨Á¢ÊÊÓÚÖ¸ÎƵĵ¯ÐÔÄ£ÐÍÔÚ°´ÕÕµ¯ÐÔ½çÏ޺еķ½·¨¼ÆËãÆ¥ÅäÉϵÄϸ½Úµã¶Ôµ«ÊÇÈçºÎÓÐЧµÄ½¨Á¢µ¯ÐÔÄ£ÐÍÈÔÊÇÒ»¸öδÄܽâ¾öµÄÎÊÌâ±¾ÎĵÄÖ¸ÎÆÆ¥Åä·½°¸¾ÍÊÇÊôÓÚ´ËÀàÐÍÈ»ºó¸ù¾Ý¶ÔÓ¦Èý½ÇÐεÄÏàËƶÈ×÷³öÅжÏ2.2 掌型识别技术与指纹浩瀚的文献相比但是却已经有一套掌型识别的商业系统在美国发行国内许多公司也购买了Recognition[35]公司的代理权在国内销售掌型技术几乎被Recognition公司所垄断却是很早就被人们所意识到的掌型(Hand-Geometry)¾ÍÊÇÀûÓÃÊֵļ¸ºÎÐÎ×´¿í¶ÈÊÖÕƵÄÐÎ×´ÐÎ×´µÈ¶¼ÊDz»ÏàͬµÄ因此可以通过掌型来验证人的身份所以掌型不能用来识别每种生物特征都有其优点和缺点对于掌型虹膜等生物特征低分辨率的光学镜头或者普通的扫描仪就可以采集录入人脸等的采集对光照条件极为敏感由于所需要提取的特征只是一些基本的几何特征对光照没有特殊要求用户的接受程度十分重要救济餐的发放等场合在这种情况下容易被用户接受但是可以肯定掌型作为一种简单易用的生物特征是有其广泛的应用空间的2.2.1 采集掌型图像相对于指纹图像的采集普通的数码相机仍需要建造相关的辅助机械设备也有实验室开发出了光学镜头的相关设备对手掌进行拍摄最终的目的都是为了得到一幅背景简单的清晰手掌的图片以便后续的提取操作采集得到的图像µÆ¹â´ÓÊÖÕÆÏ·½ÉäÀ´ÓÉÓÚ·ÅÖÃÁË4个规范手掌姿势的支撑柱这是这种拍摄方式的一个好处可以将手掌的侧面图像记录下来这种方式的缺点是同一手掌仍有可能有不同的摆放形式由于支撑柱的影响´ËÀàͼÏñÒ»°ãÊÇÓÉÊýÂëÏà»úÖ±½Ó¶ÔÊÖÕƵÄÕÆÐÄÅÄÉã»òÕßͨ¹ýÎļþɨÃèÒǵõ½µÄ²ÊÉ«RGB图像但是也必须保证五个手指是分开的这种方式对用户更为友好避免了在拍摄手掌背面时会受到指甲影响的问题在处理这类图像时当然因此这类图像对每个手掌的特征只能从手掌正面进行发掘利用Canon A75在正常光照条件下对掌心一面的手掌进行拍摄得到掌型的原图象尤其是手指的长度不会受到指甲的影响图2-5 辅助数码相机拍摄的机械设备有支撑柱的手掌图像2.2.2 掌型特征提取技术掌型的特征通常由手指的长度见图2-7ͨ³£µÄ×ö·¨ÊÇÏȽ«ÊÖÕÆͼÏñ´Ó±³¾°·ÖÀë³öÀ´È»ºó²ÉÓñ߽ç¸ú×ÙËã·¨½ÓÏÂÀ´¾ÍÊǽ«5个手指的指尖和4个指缝就可以对上述的手指长度如2.2.1节中说明的提取特征前无需对手掌进行位置校正最好将所有手掌校正成垂直的方向图2-7 常见的掌型特征量S1, S2, S3 分别为食指宽度等基本几何量以外Alexandra在进行匹配25ÕâÖÖÀûÓÃÇúÏßÐÎ×´Æ¥ÅäµÄ·½·¨Ö»ÊÇÔÚ¿ÆÑÐÉϾßÓвο¼¼ÛÖµ¾ÍÍêÈ«±³ÀëÁËÀûÓÃÊÖÕÆÌØÕ÷¼òµ¥ÕâÒ»×î»ù±¾µÄÔ-Ôò¶øÊÇÌá³öÁ˸ü¶àµÄ¼¸ºÎ²ÎÁ¿×÷ΪÕÆÐ͵ÄÌØÕ÷½øÐÐÆ¥ÅäÕÆÐ͵ÄÆ¥Åä¼¼ÊõҲȡ¾öÔÚ2.2.2节中的特征提取技术与指纹众多的特征细节点比较起来如在图2-7中这样对于每个验证的手掌然后根据门限作出判断若匹配分数低于门限值成功如[26]中采用的汉明距离(Hamming Distance)等在文献[26][27] 中引入了更为复杂的分类机Radial BasisFunction Neutral Networksµ«ÊÇͬʱҲÌá¸ßÁ˼ÆËãµÄ¸´ÔÓ¶ÈÏàÓ¦µÄ±È¶Ô·½·¨Ò²¾Í²»Í¬ÁËÈ»ºóÒÔÖÐÐĵãΪ×ø±êÔ-µã½«Á½·ùÊÖÕÆÂÖÀªÍ¼Öصþ¼ÆËãËæʱÕë¾ßÌåÆ«²î(PairwiseDistance Computation) 作为重叠误差进行匹配打分Alexandra采用食指假如能够在模板轮廓中找到一个对应的像素点从而判断这两个手掌是否一致M.Y.Liang [26] 提取一种不同的轮廓匹配方法然后利用BSpline对这个4个边缘轮廓进行拟和与几何参量的匹配算法相比于是然后利用轮廓形状信息做验证各有优势但是由于占用了更多的特征空间去描述掌型特征而采用几何参量组成的向量匹配比对过程也特别快采用这种匹配算法的另一个原因是验证速度快适合于整个系统的操作选择模式。
基于方向场的车牌倾斜校正方法
基于方向场的车牌倾斜校正方法潘仁龙;马晓娟;王林【摘要】车牌字符分割与识别是智能交通系统中的重要环节,而倾斜变形的车牌图像对车牌字符分割与识别有很大影响,为解决这个问题,提出了一种基于方向场的车牌校正方法.建议方法采用Sobel算子计算车牌图像的梯度方向场,通过对方向场角度直方图的分析找出车牌水平倾斜角,根据水平倾斜角对车牌图像进行水平旋转校正并二值化.然后,应用垂直投影法对车牌进行竖直方向上的变形校正.建议方法被用来对200幅各种情况的车牌图像进行校正,效果较好.【期刊名称】《贵州科学》【年(卷),期】2011(029)002【总页数】4页(P85-88)【关键词】车牌倾斜校正;方向场;方向角直方图;垂直投影【作者】潘仁龙;马晓娟;王林【作者单位】贵州民族学院,数学与计算机科学学院,贵阳,550025;贵州民族学院,数学与计算机科学学院,贵阳,550025;贵州民族学院,数学与计算机科学学院,贵阳,550025【正文语种】中文【中图分类】TP13;O23车牌识别是智能交通系统(ITS)的重要组成部分，可广泛应用于交通监控和管理、车牌安全防盗、高速公路和停车场收费等领域，有着广阔的应用前景。
车牌识别技术主要包括车牌定位、字符分割和字符识别3个部分。
车牌校正是车牌定位和字符分割之间的一个重要处理过程。
通常情况下，车牌图像是一个矩形，但由于摄像机和车牌之间角度的变化，常常使所拍摄的车牌图像产生倾斜现象，给字符分割带来不利影响，造成误分割和车牌识率的下降。
因此需要在字符分割之前对车牌进行倾斜矫正。
目前的车牌倾斜校正方法主要有：1)Hough变换法(郝永杰，2002;王良红，2004)。
通过Hough变换求取车牌的边框，进而确定车牌的倾斜角，或者由Hough变换提取牌照边框参数后，再求解牌照区域4个顶点的坐标，然后通过双线性空间变换对畸变图像进行校正。
但因噪声、污迹等干扰，车牌的边框常常并不明显，甚至看不到边框。
fvc医学术语
fvc医学术语FVC医学术语：为了更好地理解和交流医学领域的知识，医学术语的使用变得越来越重要。
FVC（Fingerprint Verification Competition）是指纹验证比赛的缩写。
在医学领域，FVC还可以代表肺活量测定值（Forced Vital Capacity）。
肺活量测定值是衡量肺功能的重要指标之一。
它通常通过呼气肺活量测定（Expiratory Vital Capacity）来得出。
呼气肺活量是指一个人在最大吸气后，尽力将空气全部呼出后的肺容积。
呼气肺活量测定可以通过简单的肺功能测试仪器来完成，这些仪器可以测量肺部的容积和流速。
肺活量测定值对于评估肺部健康状态非常重要。
它可以帮助医生判断是否存在肺部疾病，如肺气肿、支气管炎和哮喘等。
此外，肺活量测定值还可以用来评估肺功能的恢复情况和治疗效果，对于监测疾病的进展和预测患者的预后也具有重要意义。
为了准确测量肺活量，医生通常会给患者一些指导，如深呼吸然后迅速将空气呼出等。
测量过程中还需要注意排除一些因素的干扰，如饱食、疲劳和情绪等。
此外，还可以根据患者的年龄、性别、身高和体重等因素，参考正常肺活量范围进行评估。
肺活量测定值的结果通常以升为单位表示。
正常成年人的肺活量范围在3-5升之间，男性一般比女性略高。
如果测得的肺活量值低于正常范围，可能意味着存在肺部疾病或功能障碍。
FVC医学术语代表肺活量测定值，是评估肺功能的重要指标之一。
通过合理地使用这一术语，医生可以更准确地判断患者的肺部健康状况，并制定相应的治疗计划。
对于患者来说，了解自己的肺活量测定值也有助于更好地管理和保护自己的肺部健康。
图像处理_Fingerprint Verification Competition 2002(FVC2002)(指纹识别大赛2002)
Fingerprint Verification Competition 2002（FVC2002）(指纹识别大赛2002)数据摘要：FVC2002 is the Second International Competition for Fingerprint Verification Algorithms. The evaluation was held in April 2002 and the results of the 31 participants were presented at 16th ICPR (International Conference on Pattern Recognition). This initiative is organized by D. Maio, D. Maltoni, R. Cappelli from Biometric Systems Lab (University of Bologna), J. L. Wayman from the U.S. National Biometric Test Center (San Jose State University) and A. K. Jain from the Pattern Recognition and Image Processing Laboratory of Michigan State University.中文关键词：指纹传感器,指纹为基础的生物识别系统,指纹识别,电子指纹采集传感器,国际公开大赛,英文关键词：fingerprint sensing,fingerprint-based biometric systems,fingerprint recognition,electronic fingerprint acquisition sensors,international open competition,数据格式：IMAGE数据用途：fingerprint recognition数据详细介绍：Fingerprint Verification Competition 2002（FVC2002）FVC2002 is the Second International Competition for Fingerprint Verification Algorithms. The evaluation was held in April 2002 and the results of the 31 participants were presented at 16th ICPR (International Conference on Pattern Recognition). This initiative is organized by D. Maio, D. Maltoni, R. Cappelli from Biometric Systems Lab (University of Bologna), J. L. Wayman from the U.S. National Biometric Test Center (San Jose State University) and A. K. Jain from the Pattern Recognition and Image Processing Laboratory of Michigan State University.BackgroundThe first international competition on fingerprint verification (FVC2000) was organized in 2000. This event received a great attention both from academic and industrial biometric communities. While on the one hand, it established a common benchmark allowing developers to unambiguously compare their algorithms, on the other hand it provided an overview of the state-of-the-art infingerprint recognition. FVC2000 was undoubtedly a successful initiative as evident by the following:11 organizations participated in the contest4 different fingerprint databases were collectedThe results were presented at ICPR 2000 (International Conference on Pattern Recognition), Barcelona, September 2000.A detailed technical report was prepared and made available on the web. The report presents motivation, protocol, database collection, experiments and results of FVC2000.A CD ROM containing the four fingerprint databases and the report was prepared and more than 70 copies have been purchased by major institutions and companies working in biometrics.A web site has been created and maintained (http://bias.csr.unibo.it/fvc2000); this web site has been visited more than 11,000 times since September 14, 2000.A paper on FVC2000 has been accepted for publication in an upcoming issue of the IEEE Transaction on Pattern Analysis Machine Intelligence (PAMI). Several scientific groups are currently using FVC2000 databases in their experiments.Some companies which initially did not participate to the competition have requested us to certify their performance and to be added to the web site.The interest aroused by FVC2000 and the encouragements received, induced the organizers to schedule a new competition for 2002.AimThe continuous advances in the biometric system field and, in particular, in fingerprint techniques (both for recognition approaches and sensing devices), quickly make the performance evaluation initiatives obsolete.The aim of this competition is to track recent advances in fingerprint verification, for both academia and industry, and to provide up to date state-of-the-art in fingerprint technology.This competition should not be conceived as an official performance certification of biometric systems, since:the databases used in this contest have not been necessarily acquired in a real environment and according to a formal protocol.only parts of the system software will be evaluated by using images from sensors not native to each system.In any event, the results obtained will give a useful overview of the state-of-the-art in this field and will provide guidance to the participants for improving their algorithms.DatabasesFour different databases (DB1, DB2, DB3 and DB4) were collected by using the following sensors/technologies:∙DB1: optical sensor "TouchView II" by Identix∙DB2: optical sensor "FX2000" by Biometrika∙DB3: capacitive sensor "100 SC" by Precise Biometrics∙DB4: synthetic fingerprint generationEach database is 110 fingers wide (w) and 8 impressions per finger deep (d) (880 fingerprints in all); fingers from 101 to 110 (set B) have been made available to the participants to allow parameter tuning before the submission of the algorithms; the benchmark is then constituted by fingers numbered from 1 to 100 (set A).The following figure shows a sample image from each database:数据预览：点此下载完整数据集。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
An automatic fingerprint identification system is concerned with some or all of the following issues: • Fingerprint Acquisition: How to acquire fingerprint images and how to represent them in a proper format. • Fingerprint Verification: To determine whether two fingerprints are from the same finger. • Fingerprint Identification: To search for a query fingerprint in a database. • Fingerprint Classification: To assign a given fingerprint to one of the prespecified categories according to its geometric appearance. A number of methods are used to acquire fingerprints. Among them, the inked impression method remains the most popular. It has been essentially a standard technique for fingerprint acquisition for more than 100 years [3]. The first step in capturing an inked impression of a fingerprint is to place a few dabs of ink on a slab then rolling it out smoothly with a roller until the slab is covered with a thin, even layer of ink. Then the finger is rolled from one side of the nail to the other side over the inked slab which inks the ridge patterns on top of the finger completely. After that, the finger is rolled on a piece of paper so that the inked impression of the ridge pattern of the finger appears on the paper. Obviously, this method is time-consuming and unsuitable for an on-line fingerprint verification system. Inkless fingerprint scanners are now available which are capable of directly acquiring fingerprints in digital form. This method eliminates the intermediate digitization process of inked fingerprint impressions and makes it possible to build an on-line system. Fig. 1 shows the two inkless fingerprint scanners used in our verification system. Fingerprint images captured with the inked impression method and the inkless impression method are shown in Fig. 2.
302
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 19, NO.Verification
Anil Jain, Fellow, IEEE, Lin Hong, and Ruud Bolle, Fellow, IEEE
Abstract—Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy. Index Terms—Biometrics, fingerprints, matching, verification, minutia, orientation field, ridge extraction.
—————————— 3 ——————————
1 INTRODUCTION
F
are graphical flow-like ridges present on human fingers. They have been widely used in personal identification for several centuries [11]. The validity of their use has been well established. Inherently, using current technology fingerprint identification is much more reliable than other kinds of popular personal identification methods based on signature, face, and speech [11], [3], [15]. Although fingerprint verification is usually associated with criminal identification and police work, it has now become more popular in civilian applications such as access control, financial security, and verification of firearm purchasers and driver license applicants [11], [3]. Usually, fingerprint verification is performed manually by professional fingerprint experts. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it does not meet the performance requirements of the new applications. As a result, automatic fingerprint identification systems (AFIS) are in great demand [11]. Although significant progress has been made in designing automatic fingerprint identification systems over the past 30 years, a number of design factors (lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, fingerprint classification, etc.) create bottlenecks in achieving the desired performance [11].