香港理工大学高分辨率的指纹(HRF) 数据库_图像处理_科研数据集
基于小波神经网络的掌纹识别
摘要 ： 提出了一种基于小波神经 网络 的掌纹识别方法 。首先对掌纹 图像经过预处理得 用小波包分解的方法对该区域进行掌纹特征的提取 ， 再利用Ｒ Ｆ Ｂ 网络的容错 能力和较快的收敛性对 掌纹图
所示 。
图 ３ 小波包 分解树
然后提取 （ １，…，（， ） １ 个点的小波包分解系数Ｈ 得到 １ 个系数矩阵， ２） ， ２ ５共 ５ １ ， ５ 通过对系数矩阵进行 数据分析得到掌纹特征向量 ， 在本文中是利用求各矩阵的范数 ， 共求得 ｌ 个范数 ， ５ 组成代表此掌纹的特征 向量 ， 具体算法步骤如下 ： （） １ 选择 Ｄ ｕ ｅｈ ｓ ａｂｃｉ 小波族 中的 ｄ４ ｅ ｂ 小波和 Ｓａｎｎ ｈｎｏ 熵对大小为 １０ １０ ０ × ０ 的掌纹图像进行二级小波包
第 ２ 卷第 ３ ６ 期
２ １ 年 ５月 ００
齐 齐 哈 尔 大 学 学 报
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Ｖｏ ．６ Ｎｏ３ １ ， ． ２
Ｍａ ， ０ ０ ｙ２ １
基 于小波神经 网络 的掌纹识别
王艳春 ，李静辉 ，夏 颖
分解； 第一级小波包分解有 ４幅子图像 ， 第二级小波包分解有 １ 幅子图像 ， ３ ６ 图 中用 （ ，表示信号的第 ｆ ｉ） ， 层上的第 ， 个节点 ， 每个节点都表示一定的信号特征， 其中（，） Ｏ０ 代表经过预处理之后的原始掌纹图像 ，１） （ ， ０
．
代表了信号的低频部分 ，（， ，（２ ，（３ １ ） １ ） １） １ ， ， 代表信号的水平 ，垂直 ，以及对角方向的特征信息，相当于信 号的中高频部分 ，（， 代表了信号的第二层第 ０ ２） Ｏ 个节点的系数 ，其它通道依此类推。 （） ２ 提取小波包分解量 的（ １，…，（１） ２） ， ２５ ， 节点的系数矩阵。（０节点是纹理图像的低频部分 ，由于 ２） ， 纹理特征主要集中在中高频部分 ， 以该节点系数不考虑在 内。 所 （） ３ 求取每一系数矩阵的 ２ 范数 ，得到代表此掌纹图像 的 １ 维的特征向量 。 ５
NLPIR大数据通过知识图谱技术进行深度挖掘
NLPIR⼤数据通过知识图谱技术进⾏深度挖掘 近些年，由于以社交⽹站、基于位置的服务LBS 等为代表的新型信息产⽣⽅式的涌现，以及云计算、移动和物联⽹技术的迅猛发展，⽆处不在的移动、⽆线传感器等设备⽆时不刻都在产⽣数据，数以亿计⽤户的互联⽹服务时时刻刻都在产⽣着数据交互，⼤数据时代已经到来。
在当下，⼤数据炙⼿可热，不管是企业还是个⼈都在谈论或者从事⼤数据相关的话题与业务，我们创造⼤数据同时也被⼤数据时代包围。
在⼤量的数据中找到有意义的模式和规则。
在⼤量数据⾯前，数据的获得不再是⼀个障碍，⽽是⼀个优势。
知识图谱是以科学知识为对象，显⽰科学知识的发展进程与结构关系的⼀种图形。
科学知识图谱研究，是以科学学为研究范式，以引⽂分析⽅法和信息可视化技术为基础，涉及数学、信息科学、认知科学和计算机科学诸学科交叉的领域，是科学计量学和信息计量学的新发展。
科学知识图谱具有“图”和“谱”的双重性质与特征：既是可视化的知识图形，⼜是序列化的知识谱系，显⽰了知识元或知识群之间⽹络、结构、互动、交叉、演化或衍⽣等诸多复杂的关系。
借助科学知识图谱，⼈们可以查看庞⼤的⼈类知识体系中各个领域的结构，理顺当代知识⼤爆炸形成的复杂知识⽹络，预测科学技术知识前沿发展的新态势。
北京理⼯⼤学⼤数据搜索与挖掘实验室张华平主任研发的KGB知识图谱引擎，KGB知识图谱引擎(Knowledge Graph Builder)是基于⾃然语⾔理解、汉语词法分析，采⽤KGB语法从结构化数据与⾮结构化⽂档中抽取各类知识，⼤数据语义智能分析与知识推理，深度挖掘知识关联，实时⾼效构建知识图谱。
KGB知识图谱引擎核⼼技术与特⾊ 1 、KGB知识抽取 KGB(Knowledge Graph Builder)知识图谱引擎是我们⾃主研发的知识图谱构建与推理引擎，基于汉语词法分析的基础上，采⽤KGB语法实现了实时⾼效的知识⽣成，可以从⾮结构化⽂本中抽取各类知识，并实现了从表格中抽取指定的内容等。
设备维修信息数据挖掘
设备维修信息数据挖掘摘要随着市场竞争的日益激烈，维修售后服务成为了企业的重要竞争能力之一。
然而由于产品故障的不确定性使得备件需求难于预测，维修备件越来越多使得备件库存维护成本不断增加。
这些问题使得维修企业面临的负担加重。
因此针对产品的备件需求问题，本文利用某设备生产企业的维修数据记录，基于数据挖掘技术对不同型号的手机常见故障进行分析，从而为公司的设备储藏提供意见。
首先，本文对原始维修数据记录进行了简单分析。
在对噪声数据和“服务商代码”进行预处理之后，将数据集中的手机维修信息提取出来。
接着利用clementine12.0软件分析得知“反映问题描述”属性与手机使用时长、市场级别、服务商所在地区、产品型号相关性较强。
其次，为了分析故障与其他属性的关系，本文采用关联规则Apriori和GRI算法分析手机使用时长、产品型号分别与故障之间的关联性。
观察关联结果，发现最近买的手机（使用时间低于两个月）主要故障集中在LCD显示故障和网络故障；较早买的手机主要出现开机故障和通话故障。
但是GRI算法得出的结果支持度或置信度较低，不具有说服力。
所以本文主要利用基于协同过滤的推荐算法来分析反映问题描述属性与其他属性的关联规则，并得出了如下结果：地理位置上相近的地区，其手机常见故障也类似；不同种手机型号或不同地区的手机出现的常见故障都是：开机故障，触屏故障，按键故障和通话故障；在不同级别的市场购买手机,，其经常出现故障的手机的手机型号都是T818，T92，EG906，T912和U8。
最后，为了验证推荐算法的可信性，本文对该算法进行质量评价，利用Celmentine 将数据分为训练集和测试集，然后进行算法检验。
结果表明，推荐算法能够比较准确地得出推荐结果。
关键词：设备维修、clementine12.0软件、GRI算法、基于协同过滤的推荐算法Data mining of equipment maintenance informationAbstractAs the competition in the market is increasing, maintenance after-sale service becomes one of the important competition ability of enterprise. However, due to the uncertaint breakdown of product, the spare parts demand is difficult to predict. And with the emergence of a growing number of maintenance spare parts ,the cost of Inventory maintenance is increasing. All of these problems make maintenance enterprises are faced with the burden. Therefore, aiming at Spare parts demand for the product, we use the maintenance record of a equipment manufacturing enterprise to analyse common breakdown of different kinds of mobile phones based on data mining technology and provide equipment storage advices to the mobile phone company.First of all, the article analyses the original maintenance data records. After preprocessing the noise data and ‘Service providers code’, we extract the data set of mobile phone repair information. Then we use clementine12.0 software to analyse the correlation between the properties and learn that ‘The description of reflecting problem’ has a strong correlation with ’The usage time of mobile phone‘ , ’The market level’, ’Service area’ and ’Product model’.Then, In order to analyze the correlation between ‘The description of reflecting problem’and other attributes, We use Apriori and GRI algorithm to analyze the correlation between ’The description of reflecting problem’ and ’The usage time of mobile phone‘ , ’Product model’. Observing the correlation results,we find that the breakdown or the cellphone bought within a month is focused on the LCD display and Network fault,and the cellphone buy early appears starting up fault and communication falut mainly.However, the support or confidence of the results are so low that the results are not convincing. So we mainly use recommendation algorithm which is based on the collaborative fitering to analyse the correlation between ‘The description of reflecting proble m’and other attributes.Finally,we get the following results:1.The geographical position which is close its mobile phone common faults is similar;2. Although the product model or service area is different,the cellphone appears the same following common faults: starting up fault , touch screen fault, button fault and communication falut;3. Although the market level is different, the cellphone which appear fault usually is T818，T92，EG906，T912和U8.Finally, in order to verify the credibility of the recommendation algorithm, this article is to evaluate the quality of the algorithm.The data is divided into training set and test set used Celmentine, and then test the algorithm. The results show that, the recommendation algorithm can obtain more accurate recommendation results. Key: Equipment maintenance,Clementine12.0 software,The GRI algorithm,The recommendation algorithm which is based on the collaborative fitering目录1.挖掘目标 (7)2.分析方法与过程 (7)2.1.总体流程 (7)2.2.具体步骤 (8)2.2.1.维修数据集的特点分析 (8)2.2.2.维修数据集的预处理 (10)2.2.3.关联分析 (13)2.3.结果分析 (16)2.3.1 预处理的结果分析 (16)2.3.2手机数据集基于Clementine结果分析 (17)2.3.3 基于推荐算法的手机数据集分析 (19)2.3.4 推荐算法的评价 (25)3.结论 (26)4.参考文献 (27)5.附件 (27)1.挖掘目标本次建模目标是利用维修记录的海量真实数据，采用数据挖掘技术，分析手机各类故障与手机型号、手机各类故障与市场的相互关系，构建反映各类型号手机的常见故障评价指标体系、不同市场和地区手机质量的评价体系，为手机公司的设备储藏提供意见，同时也可为消费者提供购买意见。
高分辨率熔解HRM简介
高分辨率熔解HRM简介早在上世纪六十年代，双链DNA的熔解靠吸光度监测，数微克的DNA样品以每分钟0.1-1.0℃的速度缓缓加热，待直至数小时后完成熔解。
随后出现的利用荧光检测互补双链DNA是一个更灵敏的方法，这种方法需要先进行PCR富集DNA产物，它的流行得益于1997年问世的实时定量PCR仪Light- Cycler®。
毛细管进样和小样品体积保证了良好的温控效果，使得熔解速率大大加速到每秒钟0.1-1.0℃。
高分辨率熔解分析High Resolution Melting (HRM)于2003年发明，是一项近年来备受关注的创新的技术，实现了通过研究PCR产物依赖于序列的熔解温度而对PCR产物进行鉴定。
可用于检测样品中存在的双链DNA的单核苷酸多态性（SNPs）基因变异（Mutation）、多态性（Polymorphisms）和表观遗传学（Epigenetic）差异。
HRM分析技术的神奇之处在于它是基于对核酸双链高温熔解行为的实时监测，仅仅是在普通荧光PCR之后加一步闭管的熔解步骤，操作简便，样品可随着其本身的序列、长度、GC 含量及双链互补程度不同而被区分开。
因此，甚至是单个碱基如SNPs也可轻易被鉴定出。
原则上来说，HRM分析技术的发展得益于三项技术上的进步：1. 1. 发现了新一代的对DNA无抑制作用的饱和染料（如LC Green Plus，Resolight等）；2. 2. 高度精密的荧光监控光学系统的问世；3. 3. 发明了能够对熔解状况进行细致分析的新算法。
高分辨率熔解分析最重要的应用是基因扫描，相比传统基因分型技术，HRM具有许多优势：1. 1. 省钱—相比较测序和Taqman® SNP分型技术，HRM更适合于大规模基因分型项目；2. 2. 快速—能够在更短时间内精确分型；3. 3. 简单—易上手操作，可在高分辨率的荧光PCR仪上方便实现。
由于突变导致的突变样本熔解曲线相对于参考样本的变化是可以明显识别出来的。
fgr技术原理
fgr技术原理FGR技术原理FGR技术，即指纹识别技术，是一种通过采集和分析人类指纹特征的方法。
它基于每个人指纹的独特性，将指纹图像转化为数字特征，并进行比对，从而实现身份识别和验证。
本文将从指纹图像采集、图像处理、特征提取和比对验证等方面介绍FGR技术的原理。
一、指纹图像采集指纹图像的采集是FGR技术的第一步。
通常，指纹采集设备采用光学传感器或电容传感器进行采集。
光学传感器通过照射指纹，然后通过接收反射光来获取指纹图像；电容传感器则通过感应指纹表面的微小电荷变化来获取指纹图像。
采集到的指纹图像将被用于后续的图像处理和特征提取。
二、图像处理采集到的指纹图像通常需要经过一系列图像处理操作，以提高图像质量和准确度。
常见的图像处理操作包括增强对比度、降噪、去除图像伪迹等。
这些操作有助于消除指纹图像中的干扰因素，使得后续的特征提取和比对更加准确可靠。
三、特征提取特征提取是FGR技术的核心步骤，它将指纹图像转化为一组数字特征，用于表示指纹的独特性。
常用的特征提取算法有细节增强算法、Gabor滤波算法、方向图提取算法等。
这些算法通过分析指纹图像的细节、纹理和方向等特征，提取出具有辨识度的特征信息。
四、比对验证比对验证是指将采集到的指纹特征与已有的指纹模板进行比对，以判断是否匹配。
在比对过程中，通常会使用相似度算法，如欧氏距离算法或相关系数算法，来计算两组指纹特征之间的相似度。
如果相似度超过设定的阈值，则判定为匹配成功，否则为匹配失败。
FGR技术的原理相对简单，但在实际应用中却有着广泛的应用场景。
指纹识别技术已经被广泛应用于个人设备的解锁、身份验证、考勤管理等领域。
相比于传统的密码或卡片识别技术，指纹识别具有独特性高、方便快捷、难以伪造等优势，因此备受青睐。
然而，FGR技术也存在一些挑战和限制。
首先，指纹图像的采集受到环境因素的影响，如污垢、湿度、温度等，可能会导致采集质量下降。
其次，由于指纹图像是个人隐私信息，因此在采集、存储和传输过程中需要保证数据的安全性和隐私保护。
high resolution melting analysis -回复
high resolution melting analysis -回复什么是高分辨融解分析（High Resolution Melting Analysis, HRM）？高分辨融解分析（High Resolution Melting Analysis, HRM）是一种基于DNA的分析技术，用于检测DNA中的序列差异，如突变、多态性等。
它是一种快速、灵敏且经济高效的方法，通常用于基因突变筛查、SNP检测、基因分型等应用。
HRM利用DNA的特性，通过热反应中DNA双链解旋和再结合过程中的温度变化，来测定样品中的DNA序列。
这个过程涉及到将DNA样品在逐渐升高的温度下加热，随后监测染料的强度变化。
在整个过程中，DNA 会在特定融点温度下解旋形成单链DNA，再在高温下结合成双链DNA。
在温度梯度中，样品中的DNA序列相异点（突变、多态性等）会导致其在特定温度下有所不同的解旋和结合行为，从而导致曲线的形态和位置产生变化。
HRM的原理和步骤是什么？在HRM的实验中，需要进行一系列样品预处理、PCR扩增和融解分析。
首先，需要从样品中提取DNA，并对DNA进行质量和浓度的测定。
合适的DNA浓度对于反应的准确性和可靠性至关重要。
其次，进行PCR扩增反应。
在PCR反应中，根据待测序列的特点，选择适当的引物对DNA进行扩增，以增加待测序列（突变、SNP等）的相对丰度。
PCR反应中的温度梯度逐渐升高，使DNA经历一系列变性和重结合过程。
然后，在PCR扩增结束后，立即开始融解分析阶段。
在这个阶段，通过逐渐升高的温度，从低到高，融解PCR产物中的DNA双链结构。
同时，使用特定染料（通常是SYBR Green）来标记DNA，在特定温度下监测染料的荧光强度变化。
根据不同样本的DNA序列差异，特定的融点曲线形态和位置将发生变化。
通过分析融点曲线的形状、峰的位置和强度，可以判断样品中的DNA序列是否存在突变等差异。
HRM的优点和应用领域是什么?HRM具有以下几个优点：1. 高灵敏度和特异性：HRM可以检测到DNA序列中极小的差异，并能够区分相同长度但不同序列的DNA。
港理工 wrds数据库使用指南
港理工 wrds数据库使用指南港理工（The Hong Kong Polytechnic University）是一所位于香港的知名大学，为了提供更好的学术支持和资源，该校建立了一个名为WRDS（Wharton Research Data Services）的数据库。
本文将为您介绍如何使用港理工WRDS数据库。
港理工WRDS数据库是一个非常有用的工具，它提供了各种各样的数据集，包括金融、经济、会计和市场等领域的数据。
这些数据集可以帮助学者、研究人员和企业分析师进行深入研究和分析，从而更好地理解市场和行业的趋势和变化。
为了使用港理工WRDS数据库，您首先需要访问该数据库的官方网站。
在网站上，您需要创建一个账户，并获得访问权限。
一旦您获得了访问权限，您就可以开始使用数据库了。
在港理工WRDS数据库中，您可以根据您的需求和兴趣，选择不同的数据集进行查询和分析。
例如，如果您对金融市场感兴趣，您可以选择访问金融数据集，该数据集包含了各种金融市场的历史数据和统计信息。
如果您对经济学感兴趣，您可以选择访问经济数据集，该数据集包含了各种经济指标和变量的数据。
在使用港理工WRDS数据库时，您可以使用各种查询工具和技术来搜索和分析数据。
例如，您可以使用关键词搜索来查找特定的数据集或变量。
您还可以使用过滤器和排序功能来筛选和排序数据。
此外，您还可以使用图表和图形工具来可视化数据，以便更好地理解和解释数据。
除了查询和分析数据之外，港理工WRDS数据库还提供了一些额外的功能和资源。
例如，您可以访问论文和研究报告库，以获取最新的学术研究和行业报告。
您还可以参加培训和研讨会，以提升您的数据分析技能和知识。
总结起来，港理工WRDS数据库是一个非常有用和强大的工具，可以帮助学者、研究人员和企业分析师进行深入研究和分析。
通过使用这个数据库，您可以获得各种各样的数据集，并使用各种查询工具和技术来搜索、分析和可视化数据。
希望通过本文的介绍，您可以更好地了解和使用港理工WRDS数据库。
香港理工大学高分辨率的指纹（HRF）数据库_图像处理_科研数据集
⾹港理⼯⼤学⾼分辨率的指纹（HRF）数据库_图像处理_科研数据集⾹港理⼯⼤学⾼分辨率的指纹(HRF) 数据库(The Hong Kong Polytechnic University(PolyU)High-Resolution-Fingerprint (HRF)Database)数据介绍：Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerpr关键词：⾼分辨率的指纹,⾹港理⼯⼤学,UGC/CRC,High-Resolution-Fingerprint,PolyU,UGC/CRC,数据格式：IMAGE数据详细介绍：The Hong Kong Polytechnic University (PolyU)High-Resolution-Fingerprint (HRF) DatabaseOverview:Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerprint additional features and to the advent of high quality fingerprint imaging sensors, they have recently attracted increasing attention from researchers and practitioners working on AFRS.Our team in the Biometrics Research Centre (UGC/CRC) of the HongKong Polytechnic University has developed a high resolution fingerprintimaging device and has used it to constructed large-scale high resolutionfingerprint databases (HRF). We intend to publish our database to facilitate researchers designing effective and efficient algorithms for extracting andmatching fingerprint additional features.Fig. 1: Example additional features on fingerprints, pores, dots, and incipientridges.Description of the PolyU HRF Database:An optical fingerprint imaging device (see Fig. 2) has been built by our team.Its resolution is around 1,200dpi, and it can capture fingerprint images ofvarious sizes, e.g. 320*240 pixels and 640*480 pixels.(a) (b)Fig. 2: (a) The high resolution fingerprint imaging device we developed and (b) its inner structure.Two high resolution fingerprint image databases (denoted as DBI and DBII)have been set up by using the developed fingerprint imaging device. DBIconsists of a small training dataset and a large test dataset. The images of thesame finger in both databases were collected in two sessions which wereseparated by about two weeks. Each image is namedas “ID_S_X”.“ID” represents the identity of the person. “S” represents the session of the captured image. “X”represents the image number of each session. The following table gives the detailed information of the databases.We labeled the ground truth of sweat pores in 30 images selected from DBI. The central coordinates (row, col) of each pore were wrote into a text file (.txt). The ground truth of dots and incipients of 48 selected images were also offered. The central coordinates (row, col) of dots and two ends of each incipient were wrote into a text file (.txt). Here, the 48 selected images consists of 2 set of images ("SetIGroundTruthSampleimage" and "SetIIGroundTruthSampleimage") captured in two sessions. All of the original sample images and text files are contained in "Ground Truth.zip".Related Publication:1. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "AdaptiveFingerprint Pore Modeling and Extraction," Pattern Recognition, vol.43(8), pp. 2833-2844, 20102. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "High ResolutionFragmentary Fingerprint Alignment Using Pore-Valley Descriptors,"Pattern Recognition, vol. 43, pp. 1050-1061, 20103. Qijun Zhao, Lei Zhang, David Zhang, Nan Luo, and Jing Bao,“Adaptive Pore Model for Fingerprint Pore Extraction,” IAPR 19thInternational Conference on Pattern Recognition (ICPR2008), 20084. Qijun Zhao, Lei Zhang, David Zhang, and Nan Luo, “Direct PoreMatching for Fingerprint Recognition,” IAPR/IEEE 3rd InternationalConference on Biometrics (ICB2009), pp. 597-606, 20095. David Zhang, Feng Liu, Qijun Zhao, Guangming Lu, and Nan Luo,"Selecting a Reference High Resolution for Fingerprint RecognitionUsing Minutiae and Pores," IEEE Transactions on Instrumentation and Measurement, to appear6. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "A ComparativeStudy on Quality Assessment of High Resolution Fingerprint Images,"Proceedings of the IEEE International Conference on Image Processing (ICIP2010), Hong Kong, September 20107. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "Parallel versusHierarchical Fusion of Extended Fingerprint Features," Proceedings ofthe IAPR 20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20108. Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, "Fingerprint PoreMatching based on Sparse Representation," Proceedings of the IAPR20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20109. Q. Zhao, Lei Zhang, David Zhang, Wenyi Huang, and Jian Bai,“Curvature and Singularity Driven Diffusion for Oriented PatternEnhancement with Singular Points,” CVPR09. Proceedings of IEEEConference on Computer Vision and Pattern Recognition, pp. 1-7,Miami, Florida, USA, June 22-24 2009.The Announcement of the CopyrightAll rights of the PolyU HRF Database are reserved. The database is only available for research and noncommercial purposes. Commercial distribution or any act related to commercial use of this database is strictly prohibited. A clear acknowledgement should be made for any public work based on the PolyU HRF Database. A citation to "PolyU HRF Database, /doc/399f7c6fa45177232f60a2bf.html .hk/~biometrics/HRF/HRF.htm” and our related works must be added in the references. A soft copy of any released or public documents that use the PolyU HRF Database must be forwardedto: cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hkDownloading Steps:Download “HRF DBI.zip”, “HRF DBII.zip”, or "Ground Truth.zip"to your local disk. Then, fill in the application forms. Send the application formto cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hk. The successful applicants will receive the passwords for unzipping the files downloaded.HRF Databases:HRF DBI.zipHRF DBII.zipGround Truth.zipContact Information:Lei ZHANG, Associate ProfessorBiometric Research Centre (UGC/CRC)The Hong Kong Polytechnic UniversityHung Hom, Kowloon, Hong KongE-mail: cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hk数据预览：点此下载完整数据集。
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香港理工大学高分辨率的指纹(HRF) 数据库(The Hong Kong Polytechnic University(PolyU)High-Resolution-Fingerprint (HRF)Database)数据介绍：Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerpr关键词：高分辨率的指纹,香港理工大学,UGC/CRC,High-Resolution-Fingerprint,PolyU,UGC/CRC,数据格式：IMAGE数据详细介绍：The Hong Kong Polytechnic University (PolyU)High-Resolution-Fingerprint (HRF) DatabaseOverview:Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerprint additional features and to the advent of high quality fingerprint imaging sensors, they have recently attracted increasing attention from researchers and practitioners working on AFRS.Our team in the Biometrics Research Centre (UGC/CRC) of the HongKong Polytechnic University has developed a high resolution fingerprintimaging device and has used it to constructed large-scale high resolutionfingerprint databases (HRF). We intend to publish our database to facilitate researchers designing effective and efficient algorithms for extracting andmatching fingerprint additional features.Fig. 1: Example additional features on fingerprints, pores, dots, and incipientridges.Description of the PolyU HRF Database:An optical fingerprint imaging device (see Fig. 2) has been built by our team.Its resolution is around 1,200dpi, and it can capture fingerprint images ofvarious sizes, e.g. 320*240 pixels and 640*480 pixels.(a) (b)Fig. 2: (a) The high resolution fingerprint imaging device we developed and (b) its inner structure.Two high resolution fingerprint image databases (denoted as DBI and DBII)have been set up by using the developed fingerprint imaging device. DBIconsists of a small training dataset and a large test dataset. The images of thesame finger in both databases were collected in two sessions which wereseparated by about two weeks. Each image is namedas “ID_S_X”.“ID” represents the identity of the person. “S” represents the session of the captured image. “X”represents the image number of each session. The following table gives the detailed information of the databases.We labeled the ground truth of sweat pores in 30 images selected from DBI. The central coordinates (row, col) of each pore were wrote into a text file (.txt). The ground truth of dots and incipients of 48 selected images were also offered. The central coordinates (row, col) of dots and two ends of each incipient were wrote into a text file (.txt). Here, the 48 selected images consists of 2 set of images ("SetIGroundTruthSampleimage" and "SetIIGroundTruthSampleimage") captured in two sessions. All of the original sample images and text files are contained in "Ground Truth.zip".Related Publication:1. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "AdaptiveFingerprint Pore Modeling and Extraction," Pattern Recognition, vol.43(8), pp. 2833-2844, 20102. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "High ResolutionFragmentary Fingerprint Alignment Using Pore-Valley Descriptors,"Pattern Recognition, vol. 43, pp. 1050-1061, 20103. Qijun Zhao, Lei Zhang, David Zhang, Nan Luo, and Jing Bao,“Adaptive Pore Model for Fingerprint Pore Extraction,” IAPR 19thInternational Conference on Pattern Recognition (ICPR2008), 20084. Qijun Zhao, Lei Zhang, David Zhang, and Nan Luo, “Direct PoreMatching for Fingerprint Recognition,” IAPR/IEEE 3rd InternationalConference on Biometrics (ICB2009), pp. 597-606, 20095. David Zhang, Feng Liu, Qijun Zhao, Guangming Lu, and Nan Luo,"Selecting a Reference High Resolution for Fingerprint RecognitionUsing Minutiae and Pores," IEEE Transactions on Instrumentation and Measurement, to appear6. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "A ComparativeStudy on Quality Assessment of High Resolution Fingerprint Images,"Proceedings of the IEEE International Conference on Image Processing (ICIP2010), Hong Kong, September 20107. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "Parallel versusHierarchical Fusion of Extended Fingerprint Features," Proceedings ofthe IAPR 20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20108. Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, "Fingerprint PoreMatching based on Sparse Representation," Proceedings of the IAPR20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20109. Q. Zhao, Lei Zhang, David Zhang, Wenyi Huang, and Jian Bai,“Curvature and Singularity Driven Diffusion for Oriented PatternEnhancement with Singular Points,” CVPR09. Proceedings of IEEEConference on Computer Vision and Pattern Recognition, pp. 1-7,Miami, Florida, USA, June 22-24 2009.The Announcement of the CopyrightAll rights of the PolyU HRF Database are reserved. The database is only available for research and noncommercial purposes. Commercial distribution or any act related to commercial use of this database is strictly prohibited. A clear acknowledgement should be made for any public work based on the PolyU HRF Database. A citation to "PolyU HRFDatabase, .hk/~biometrics/HRF/HRF.htm” and our related works must be added in the references. A soft copy of any released or public documents that use the PolyU HRF Database must be forwardedto: cslzhang@.hkDownloading Steps:Download “HRF DBI.zip”, “HRF DBII.zip”, or "Ground Truth.zip"to your local disk. Then, fill in the application forms. Send the application formto cslzhang@.hk. The successful applicants will receive the passwords for unzipping the files downloaded.HRF Databases:HRF DBI.zipHRF DBII.zipGround Truth.zipContact Information:Lei ZHANG, Associate ProfessorBiometric Research Centre (UGC/CRC)The Hong Kong Polytechnic UniversityHung Hom, Kowloon, Hong KongE-mail: cslzhang@.hk数据预览：点此下载完整数据集。