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1
Yager, Neil Gordon Computer Science & Engineering Faculty of Engineering UNSW. "Hierarchical fingerprint verification". Awarded by:University of New South Wales. Computer Science and Engineering, 2006. http://handle.unsw.edu.au/1959.4/27008.
Testo completoAbstract (sommario):
Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint based identification in the early 1960's. More recently, fingerprints have found an application in the emerging industry of biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioral characteristics. Due to its security related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. However, despite decades of intense research there are still significant challenges for the developers of automated fingerprint verification systems. This thesis includes an examination of all major stages of the fingerprint verification process, with contributions made at each step. The primary focus is upon fingerprint registration, which is the challenging problem of aligning two prints in order to compare their corresponding features for verification. A hierarchical approach is proposed consisting of three stages, each of which employs novel features and techniques for alignment. Experimental results show that the hierarchical approach is robust and outperforms competing state-of-the-art registration methods from the literature. However, despite its power, like most algorithms it has limitations. Therefore, a novel method of information fusion at the registration level has been developed. The technique dynamically selects registration parameters from a set of competing algorithms using a statistical framework. This allows for the relative advantages of different approaches to be exploited. The results show a significant improvement in alignment accuracy for a wide variety of fingerprint databases. Given a robust alignment of two fingerprints, it still remains to be verified whether or not they have originated from the same finger. This is a non-trivial problem, and a close examination of fingerprint features available for this task is conducted with extensive experimental results.
2
Deng, Huimin. "Robust minutia-based fingerprint verification". Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37036427.
Testo completo
3
Deng, Huimin, e 鄧惠民. "Robust minutia-based fingerprint verification". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37036427.
Testo completo
4
Yan, Weiwei. "Software-hardware Cooperative Embedded Verification System Fusing Fingerprint Verification and Shared-key Authentication". Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-66677.
Testo completoAbstract (sommario):
In order to protect the security of the commercial information, personnel information, military information, governmental information on the Internet, the claimed identity should be authenticated. Now there are three main security authentication methods: first: using user PIN, such as password; second: using physical key, such as USBKey; third: using biological authentication technology, such as fingerprint, iris, voice and palm prints, etc. Because of the uniqueness, invariance, and ubiquity properties of biometric authentication, biometric authentication is becoming popular, especially fingerprint recognition. However, when the fingerprint recognition information is transported on the public channel, it may be attacked, such as the fingerprint information is stolen. So a cryptology mechanism is needed to protect the fingerprint recognition information. In the field of embedded security authentication system, the traditional hardware implementation mechanism, such as ASIC, can satisfy requires of functions and performances, but it is not configurable, flexible, and easy to expand; the traditional software implementation mechanism, such as general purpose processor, is flexible, but the cost and the power consumption are higher than hardware implementation. In order to take the advantages of biometrics, cryptology, hardware implementation, and software implementation, a hardware-software cooperating embedded authentication system based on shared-key authentication and fingerprint verification is proposed. First, this system authenticates the identities of client and server by shared-key authentication, creates the current encrypt key and hash key, and then authenticates the identity of them via fingerprint recognition. During fingerprint recognition, the information of fingerprint is not needed to transmit over the public channel, so the security of fingerprint is increased. Theoretic analysis and experiments show that, this system reach very high authentication rate and security. This system can resist replay attack, server template attack, device template attack, effectively.
5
Sandström, Marie. "Liveness Detection in Fingerprint Recognition Systems". Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2397.
Testo completoAbstract (sommario):
Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods available today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today.
Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live fingers and artificial clones. The first approach is a literature study, while the second consists of experiments.
A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pressure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.
The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems werecircumvented with all subjects'artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.
6
Fransson, Linda, e Therese Jeansson. "Biometric methods and mobile access control". Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5023.
Testo completoAbstract (sommario):
Our purpose with this thesis was to find biometric methods that can be used in access control of mobile access. The access control has two parts. Firstly, to validate the identity of the caller and, secondly, to ensure the validated user is not changed during the session that follows. Any solution to the access control problem is not available today, which means that anyone can get access to the mobile phone and the Internet. Therefore we have researched after a solution that can solve this problem but also on how to secure that no one else can take over an already validated session. We began to search for biometric methods that are available today to find them that would be best suited together with a mobile phone. After we had read information about them we did choose three methods for further investigation. These methods were Fingerprint Recognition, Iris Scan and Speaker Verification. Iris Scan is the method that is best suited to solve the authentication problem. The reasons for this are many. One of them is the uniqueness and stability of the iris, not even identical twins or the pair of the same individual has the same iris minutiae. The iris is also very protected behind eyelids, cornea and the aqueous humor and therefore difficult to damage. When it comes to the method itself, is it one of the most secure methods available today. One of the reasons for this is that the equal error rate is better than one in a million. However, this rate can be even better. It all depends on the Hamming Distance, which is a value that show how different the saved and temporarily template are, and what it is set to. To solve our session authentication, which was to make sure that no one else could take over a connected mobile phone, a sensor plate is the answer. This sensor will be able to sense for touch, heat and pulse. These three sensor measurements will together secure a validated session since the mobile phone will disconnect if the sensor looses its sensor data. There are, however, technological and other challenges to be solved before our proposed solutions will become viable. We address some of these issues in our thesis.
7
Sun, Wei [Verfasser], Thomas [Akademischer Betreuer] Sikora, Thomas [Gutachter] Sikora, Atta [Gutachter] Badii e Ivo [Gutachter] Keller. "Low complexity embedded fingerprint verification and identification system / Wei Sun ; Gutachter: Thomas Sikora, Atta Badii, Ivo Keller ; Betreuer: Thomas Sikora". Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156181704/34.
Testo completo
8
Dimitrov, Emanuil. "Fingerprints recognition". Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-5522.
Testo completoAbstract (sommario):
Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.
9
Falade, Joannes Chiderlos. "Identification rapide d'empreintes digitales, robuste à la dissimulation d'identité". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC231.
Testo completoAbstract (sommario):
La biométrie est de plus en plus utilisée à des fins d’identification compte tenu de la relation étroite entre la personne et son identifiant (comme une empreinte digitale). Nous positionnons cette thèse sur la problématique de l’identification d’individus à partir de ses empreintes digitales. L’empreinte digitale est une donnée biométrique largement utilisée pour son efficacité, sa simplicité et son coût d’acquisition modeste. Les algorithmes de comparaison d’empreintes digitales sont matures et permettent d’obtenir en moins de 500 ms un score de similarité entre un gabarit de référence (stocké sur un passeport électronique ou une base de données) et un gabarit acquis. Cependant, il devient très important de déterminer l'identité d'un individu contre une population entière en un temps très court (quelques secondes). Ceci représente un enjeu important compte tenu de la taille de la base de données biométriques (contenant un ensemble d’individus de l’ordre d’un pays). Par exemple, avant de délivrer un nouveau passeport à un individu qui en fait la demande, il faut faire une recherche d'identification sur la base des données biométriques du pays afin de s'assurer que ce dernier n'en possède pas déjà un autre mais avec les mêmes empreintes digitales (éviter les doublons). Ainsi, la première partie du sujet de cette thèse concerne l’identification des individus en utilisant les empreintes digitales. D’une façon générale, les systèmes biométriques ont pour rôle d’assurer les tâches de vérification (comparaison 1-1) et d’identification (1-N). Notre sujet se concentre sur l’identification avec N étant à l’échelle du million et représentant la population d’un pays par exemple. Dans le cadre de nos travaux, nous avons fait un état de l’art sur les méthodes d’indexation et de classification des bases de données d’empreintes digitales. Nous avons privilégié les représentations binaires des empreintes digitales pour indexation. Tout d’abord, nous avons réalisé une étude bibliographique et rédigé un support sur l’état de l’art des techniques d’indexation pour la classification des empreintes digitales. Ensuite, nous avons explorer les différentes représentations des empreintes digitales, puis réaliser une prise en main et l’évaluation des outils disponibles à l’imprimerie Nationale (IN Groupe) servant à l'extraction des descripteurs représentant une empreinte digitale. En partant de ces outils de l’IN, nous avons implémenté quatre méthodes d’identification sélectionnées dans l’état de l’art. Une étude comparative ainsi que des améliorations ont été proposées sur ces méthodes. Nous avons aussi proposé une nouvelle solution d'indexation d'empreinte digitale pour réaliser la tâche d’identification qui améliore les résultats existant. Les différents résultats sont validés sur des bases de données de tailles moyennes publiques et nous utilisons le logiciel Sfinge pour réaliser le passage à l’échelle et la validation complète des stratégies d’indexation. Un deuxième aspect de cette thèse concerne la sécurité. Une personne peut avoir en effet, la volonté de dissimuler son identité et donc de mettre tout en œuvre pour faire échouer l’identification. Dans cette optique, un individu peut fournir une empreinte de mauvaise qualité (portion de l’empreinte digitale, faible contraste en appuyant peu sur le capteur…) ou fournir une empreinte digitale altérée (empreinte volontairement abîmée, suppression de l’empreinte avec de l’acide, scarification…). Il s'agit donc dans la deuxième partie de cette thèse de détecter les doigts morts et les faux doigts (silicone, impression 3D, empreinte latente) utilisés par des personnes mal intentionnées pour attaquer le système. Nous avons proposé une nouvelle solution de détection d'attaque basée sur l'utilisation de descripteurs statistiques sur l'empreinte digitale. Aussi, nous avons aussi mis en place trois chaînes de détections des faux doigts utilisant les techniques d'apprentissages profondsBiometrics are increasingly used for identification purposes due to the close relationship between the person and their identifier (such as fingerprint). We focus this thesis on the issue of identifying individuals from their fingerprints. The fingerprint is a biometric data widely used for its efficiency, simplicity and low cost of acquisition. The fingerprint comparison algorithms are mature and it is possible to obtain in less than 500 ms a similarity score between a reference template (enrolled on an electronic passport or database) and an acquired template. However, it becomes very important to check the identity of an individual against an entire population in a very short time (a few seconds). This is an important issue due to the size of the biometric database (containing a set of individuals of the order of a country). Thus, the first part of the subject of this thesis concerns the identification of individuals using fingerprints. Our topic focuses on the identification with N being at the scale of a million and representing the population of a country for example. Then, we use classification and indexing methods to structure the biometric database and speed up the identification process. We have implemented four identification methods selected from the state of the art. A comparative study and improvements were proposed on these methods. We also proposed a new fingerprint indexing solution to perform the identification task which improves existing results. A second aspect of this thesis concerns security. A person may want to conceal their identity and therefore do everything possible to defeat the identification. With this in mind, an individual may provide a poor quality fingerprint (fingerprint portion, low contrast by lightly pressing the sensor...) or provide an altered fingerprint (impression intentionally damaged, removal of the impression with acid, scarification...). It is therefore in the second part of this thesis to detect dead fingers and spoof fingers (silicone, 3D fingerprint, latent fingerprint) used by malicious people to attack the system. In general, these methods use machine learning techniques and deep learning. Secondly, we proposed a new presentation attack detection solution based on the use of statistical descriptors on the fingerprint. Thirdly, we have also build three presentation attacks detection workflow for fake fingerprint using deep learning. Among these three deep solutions implemented, two come from the state of the art; then the third an improvement that we propose. Our solutions are tested on the LivDet competition databases for presentation attack detection
10
Bartoň, Jaroslav. "Podpora pro autentizaci pomocí otisků prstu". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-235492.
Testo completoAbstract (sommario):
The goal of the thesis is the finger-print authentication support within the Linux operating system and the K Desktop Environment (KDE). Theoretical part of the thesis firstly explains main IT security terms and ways to proof the identity. Secondly it describes biometric systems and types of processed biometric characteristics. Lastly the features of finger-prints, their markants as well as types of scanners used in scanning the finger-prints and ways to analyze the scanned material have been elaborated. Practical solution part of the thesis develops and establishes finger-print management application and plugin for KDM graphics login manager.
11
Wales, Chris. "Identifying digital fingerprints in source code for authorship verification /". Leeds : University of Leeds, School of Computer Studies, 2008. http://www.comp.leeds.ac.uk/fyproj/reports/0708/Wales.pdf.
Testo completo
12
Kazík, Martin. "Zpracování otisků prstů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218978.
Testo completoAbstract (sommario):
This diploma thesis deals with the identification of persons by their fingerprints. In the beginning, there is a brief overview of biometric methods. The issue of fingerprints and their automatic processing is very extensive and addresses a large amount of literature. This work describes the various stages processing fingerprints from receiving a fingerprint scan through the methods of enhancing fingerprint, binarization, the detection of minutiae to compare two fingerprints. After a short introduction of biometric techniques is briefly introduced the history of fingerprints as an instrument for identification of persons. Subsequently, there is the description of problems of obtaining fingerprints from older methods that require the transmission medium, to methods of obtaining fingerprints directly into a digital format. There are described basic principles of sensors and the principle of their function. Next part deals with methods of processing digital fingerprint images. These are methods for fingerprint image enhancement, methods for image thresholding, thinning of ridge lines, detection of global and local characteristics of the fingerprint. The next section introduces the basic methods of comparison and classification of fingerprints. The practical part describes the specific implementation of fingerprint processing chain. For each processing stage is implemented one selected method. The result is an application that allows displaying the output of each phase, the comparison of fingerprints 1:1 and also finds a fingerprint in the database, by comparing one fingerprint with many.
13
Kovář, Martin. "Snímač otisku prstu". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221330.
Testo completoAbstract (sommario):
This master’s thesis deals with the issue of scanning human fingerprints, which is currently very topical and represents the most widespread biometric technology. The theoretical part of the work acquaints the reader with basics of dactyloscopy and biometrics and concerns technologies used for fingerprinting, image preprocessing methods and commercially available contactless optical scanners. The practical part is a realisation of a contactless optical scanner based on a Raspberry Pi minicomputer, implementation of preprocessing algorithms in Python and testing of the device from the perspective of image quality.
14
Wu, Di. "A Global Spatial Model for Loop Pattern Fingerprints and Its Spectral Analysis". Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538788/.
Testo completoAbstract (sommario):
The use of fingerprints for personal identification has been around for thousands of years (first established in ancient China and India). Fingerprint identification is based on two basic premises that the fingerprint is unique to an individual and the basic characteristics such as ridge pattern do not change over time. Despite extensive research, there are still mathematical challenges in characterization of fingerprints, matching and compression. We develop a new mathematical model in the spatial domain for globally modeling loop pattern fingerprints. Although it is based on the well-known AM-FM (amplitude modulation and frequency modulation) image representation, the model is constructed by a global mathematical function for the continuous phase and it provides a flexible parametric model for loop pattern fingerprints. In sharp contrast to the existing methods, we estimate spatial parameters from the spectral domain by combining the exact values of frequencies with their orientations perpendicular to the fingerprint ridge flow. In addition, to compress fingerprint images and test background Gaussian white noise, we propose a new method based on periodogram spacings. We obtain the joint pdf of these m-dependent random variables at Fourier frequencies and derive the asymptotic distribution of the test statistic.
15
Chikkerur, Sharat S. "Online fingerprint verification system". 2005. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:1426769.
Testo completoAbstract (sommario):
Thesis (M.S.)--State University of New York at Buffalo, 2005.Title from PDF title page (viewed on Feb. 2, 2006) Available through UMI ProQuest Digital Dissertations. Thesis adviser: Alexander N. Cartwright.
16
Ding, Chian-Chuang, e 丁鎮權. "Fingerprint Verification System Design". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/61180923557915950456.
Testo completoAbstract (sommario):
碩士淡江大學
電機工程學系
91
With the advance of new technologies some biological features, such as pupil, face, fingerprint, have been employed for developing personal identification systems. Among all, fingerprint identification is one of the most commonly used. Beside to the uniqueness and the time invariance properties of fingerprints necessary for identification, the costs and the performance of fingerprint identification methods are quite comparable with the methods using other biological features. Fingerprint identification also plays an important role in law’s identity. Therefore how to correctly differentiate two fingerprints is very important on the issue of personal identification. Conventionally, a series of preprocessing are required in order to facilitate the extraction of fingerprint features for identification. Then further steps will be taken based on the locations as well as the number of the features points. Very often, noisy spots were generated during the acquisition process of the fingerprint image. This significantly alters the number of endpoints and bifurcation points. Therefore it often leads to wrong conclusions when the subsequent matching test is performed. In this paper, we use 8×8 masks to search for the local directions of the fingerprint, based on which we can locate the position of the core point. Then gray-level normalization is performed on the fan-shaped regions around the core point. Finally, we perform image enhancement on eight fixed directions using Gabor filters. The variances of the fan-shaped regions are taken as the feature values of the fingerprint. In the phase of identification, Euclidean distance is adopted to measure the difference between the fingerprints under consideration in order to determine whether they are identical. Finally, we will integrate the proposed procedures and establish a PC-based environment for simulating the real fingerprint identification processes, which consists of the fingerprint data recording and the matching test, in order to illustrate its efficiency and effectiveness in practice.
17
Chang, Chia-Yung, e 張嘉勇. "Automatic Fingerprint Verification System". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/09665079293715705261.
Testo completoAbstract (sommario):
碩士國立清華大學
資訊工程學系
91
Fingerprints are considered as an efficient and viable means of biometrics. An automatic fingerprint verification system (AFVS) can automatically verify an individual based on minutiae matching. The fingerprint verification contains two main stages, fingerprint classification and fingerprint matching. In the stage of fingerprint classification, fingerprints are classified into four classes to reduce the following fingerprint matching. The four classes are arch, left loop, right loop and whorl. In the stage of fingerprint matching, fingerprint minutiae must be detected for matching. A minutiae pattern consists of ridge endings and bifurcations. Based on the differences of distance and direction of minutiae, we compute matching scores of all pair of fingerprints. If one of the matching scores with the other 3 fingerprints acquired from the same individual is among the top 3 places, the fingerprint is accepted for the verification. The error rate of our AFVS tested on the database DBLI101 is 17.08% (69/404) and the other database of 28 individuals gains the error rate of 0.98% (1/112)
18
"Fast fingerprint verification using sub-regions of fingerprint images". 2004. http://library.cuhk.edu.hk/record=b5892013.
Testo completoAbstract (sommario):
Chan Ka Cheong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 77-85).
Abstracts in English and Chinese.
Chapter 1. --- Introduction --- p.1
Chapter 1.1 --- Introduction to Fingerprint Verification --- p.1
Chapter 1.1.1 --- Biometrics --- p.1
Chapter 1.1.2 --- Fingerprint History --- p.2
Chapter 1.1.3 --- Fingerprint characteristics --- p.4
Chapter 1.1.4 --- A Generic Fingerprint Matching System Architecture --- p.6
Chapter 1.1.5 --- Fingerprint Verification and Identification --- p.8
Chapter 1.1.7 --- Biometric metrics --- p.10
Chapter 1.2 --- Embedded system --- p.12
Chapter 1.2.1 --- Introduction to embedded systems --- p.12
Chapter 1.2.2 --- Embedded systems characteristics --- p.12
Chapter 1.2.3 --- Performance evaluation of a StrongARM processor --- p.13
Chapter 1.3 --- Objective -An embedded fingerprint verification system --- p.16
Chapter 1.4 --- Organization of the Thesis --- p.17
Chapter 2 --- Literature Reviews --- p.18
Chapter 2.1 --- Fingerprint matching overviews --- p.18
Chapter 2.1.1 --- Minutiae-based fingerprint matching --- p.20
Chapter 2.2 --- Fingerprint image enhancement --- p.21
Chapter 2.3 --- Orientation field Computation --- p.22
Chapter 2.4 --- Fingerprint Segmentation --- p.24
Chapter 2.5 --- Singularity Detection --- p.25
Chapter 2.6 --- Fingerprint Classification --- p.27
Chapter 2.7 --- Minutia extraction --- p.30
Chapter 2.7.1 --- Binarization and thinning --- p.30
Chapter 2.7.2 --- Direct gray scale approach --- p.32
Chapter 2.7.3 --- Comparison of the minutiae extraction approaches --- p.35
Chapter 2.8 --- Minutiae matching --- p.37
Chapter 2.8.1 --- Point matching --- p.37
Chapter 2.8.2 --- Structural matching technique --- p.38
Chapter 2.9 --- Summary --- p.40
Chapter 3. --- Implementation --- p.41
Chapter 3.1 --- Fast Fingerprint Matching System Overview --- p.41
Chapter 3.1.1 --- Typical Fingerprint Matching System --- p.41
Chapter 3.1.2. --- Fast Fingerprint Matching System Overview --- p.41
Chapter 3.2 --- Orientation computation --- p.43
Chapter 3.21 --- Orientation computation --- p.43
Chapter 3.22 --- Smooth orientation field --- p.43
Chapter 3.3 --- Fingerprint image segmentation --- p.45
Chapter 3.4 --- Reference Point Extraction --- p.46
Chapter 3.5 --- A Classification Scheme --- p.51
Chapter 3.6 --- Finding A Small Fingerprint Matching Area --- p.54
Chapter 3.7 --- Fingerprint Matching --- p.57
Chapter 3.8 --- Minutiae extraction --- p.59
Chapter 3.8.1 --- Ridge tracing --- p.59
Chapter 3.8.2 --- cross sectioning --- p.60
Chapter 3.8.3 --- local maximum determination --- p.61
Chapter 3.8.4 --- Ridge tracing marking --- p.62
Chapter 3.8.5 --- Ridge tracing stop criteria --- p.63
Chapter 3.9 --- Optimization technique --- p.65
Chapter 3.10 --- Summary --- p.66
Chapter 4. --- Experimental results --- p.67
Chapter 4.1 --- Experimental setup --- p.67
Chapter 4.2 --- Fingerprint database --- p.67
Chapter 4.3 --- Reference point accuracy --- p.67
Chapter 4.4 --- Variable number of matching minutiae results --- p.68
Chapter 4.5 --- Contribution of the verification prototype --- p.72
Chapter 5. --- Conclusion and Future Research --- p.74
Chapter 5.1 --- Conclusion --- p.74
Chapter 5.2 --- Future Research --- p.74
Bibliography --- p.77
19
Chen, Ming-Zong, e 陳銘宗. "Feature Points Fingerprint Verification System". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/62070343839797608189.
Testo completoAbstract (sommario):
碩士清雲科技大學
電機工程所
99
Biometrics is using biological features to verify the user''s own identity, and these features include voice, face, fingerprint, iris ... and so on. The fingerprint feature is the most commonly used research method in personal verification. In addition to the fingerprint keeps the same during a long period of time, the implementation of the function is more convenience and more practical than other verification methods. In this study, we modify the existing algorithms to designed a verification system using C + + programming language. We use the capacitive or optical fingerprint capture devices to capture fingerprint image. The captured fingerprint image first will do pre-processing, and then find the core point of the image. Using this core point center the image is divided into blocks, the size of each block is 7 x 7 pixels. Identify features in each block and locate all features in the image by using the core point as the reference points. Use this information as a basis for the implementation of ID verification can improve the drawback that feature points are too close together or the fingerprint image is incomplete to avoid verification errors.
20
yu, yang shih, e 楊詩郁. "A Study of Fingerprint Verification". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/21144669747974241067.
Testo completoAbstract (sommario):
碩士淡江大學
資訊工程學系
88
Various technological approaches for the identification of individuals have been proposed and fingerprint identification is one of the most reliable approaches. In the past, several organizations have used fingerprint identification successfully for not only the investigation of criminals but also to control access to restricted data or areas. Recently, the demand for automatic identification is increasing in the business world for transactions and individual''s security. The advantage of the fingerprint identification is that it contains two convenient secure characteristics can not be forged and always with the individual. The reason is no two people have the same fingerprints and the fingerprint is easier to carry than an identification card. Because computer techniques are becoming more and more convenient. There is more and more important data that must maintained computers and exchanged by networks. Therefore, a fingerprint identification system can play an important role in system security and identification verification, and this can prove the importance of the fingerprint identification. Most automatic systems for fingerprint comparison are based on minutiae matching .Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. Automatic minutiae detection is an extremely critical process, especially in low_quality fingerprints where noise and contract deficiency can originate pixel configuration similar to minutiae or hide real minutiae. Several approaches have been proposed in the literature; although rather different from each other, all the methods are based on a lot of preprocessing, however , it consumes time. In this work we propose an original technique, based on ridge line following, where the minutiae are extracted directly from gray scale images without a series of preprocessing.In spite of a greater conceptual complexity, the method proposed performs better both in terms of efficiency and robustness.
21
"Towards more robust fingerprint verification". 2005. http://library.cuhk.edu.hk/record=b5892379.
Testo completoAbstract (sommario):
Yeung Hoi Wo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 81-88).
Abstracts in English and Chinese.
Chapter 1. --- Introduction --- p.10
Chapter 1.1 --- Biometric Systems --- p.1
Chapter 1.2 --- Comparison of Biometrics --- p.2
Chapter 1.3 --- Introduction of Fingerprint --- p.6
Chapter 1.3.1 --- History of Fingerprint --- p.6
Chapter 1.3.2 --- Fingerprint Analysis --- p.8
Chapter 1.4 --- Fingerprint Verification --- p.13
Chapter 1.4.1 --- Correlation Based Verification: --- p.13
Chapter 1.4.2 --- Minutiae Based Verification: --- p.15
Chapter 1.4.3 --- Ridge Feature-Based Verification: --- p.16
Chapter 1.5 --- Evaluation of Verification Systems --- p.17
Chapter 1.6 --- Difficulties of Fingerprint Verification --- p.22
Chapter 1.7 --- Contributions --- p.25
Chapter 1.8 --- Organization of the Thesis --- p.26
Chapter 2. --- Two-Pass Direct Gray-Scale for Minutiae Detection --- p.28
Chapter 2.1 --- Introduction --- p.28
Chapter 2.2 --- Background Information --- p.29
Chapter 2.3 --- Two-Pass Direct Gray Scale --- p.34
Chapter 2.3.1 --- First Pass of TPD --- p.38
Chapter 2.3.2 --- Second Pass of TPD --- p.41
Chapter 2.4 --- Other Implementation Details --- p.44
Chapter 2.4.1 --- Foreground Detection --- p.45
Chapter 2.4.2 --- Region of Interest Detection --- p.48
Chapter 2.4.3 --- Matching Methodology --- p.52
Chapter 2.5 --- Experimental Results --- p.58
Chapter 2.6 --- Summary --- p.62
Chapter 3. --- Image Mosaicking and Template Synthesis --- p.63
Chapter 3.1 --- Introduction --- p.63
Chapter 3.2 --- Background Information --- p.65
Chapter 3.3 --- Template Synthesis and Image Mosaicking --- p.66
Chapter 3.3.1 --- Template Alignment --- p.66
Chapter 3.3.2 --- Template Synthesis --- p.68
Chapter 3.3.3 --- Image Mosaicking --- p.70
Chapter 3.4 --- Experiments --- p.72
Chapter 3.5 --- Summary --- p.75
Chapter 4. --- Conclusion and Future Investigations --- p.77
References --- p.81
22
Chia-Chi, Wu, e 吳家頎. "Fingerprint Verification System on SoC". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/01575945899273418139.
Testo completoAbstract (sommario):
碩士國立臺灣科技大學
資訊工程系
91
Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and inefficiency. Therefore, 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. This paper describes the design and implementation of a fingerprint verification system on System on a Chip (SoC). Our fingerprint verification system operates in four stages: fingerprint acquisition, image pre-processing, minutiae extraction and minutiae matching. Image pre-processing consists of segmentation, smoothing and thinning, which help for extract significant minutiae more accurately. The minutiae extraction algorithm is implemented for extracting features from an input fingerprint image captured with the sensor. For our fingerprint verification system in this paper, we use ridge endings and ridge bifurcations for fingerprint matching. We use Altera’s Nios SoC development kit to implement fingerprint verification system. We build hardware core, including CPU and peripheral modules, with System On a Programmable Chip (SOPC) Builder, and implement fingerprint verification algorithm using C programming language. We also use Programming Language Interface (PLI) routine to proceed hardware & software co-verification in high-level language. To make sure the system be correct, we use UART which communicates PC with the Nios development board to verify the accuracy of SoC.
23
Chen, Po-Jui. "Development of Automatic Fingerprint Verification Systems". 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2707200402425600.
Testo completo
24
Chen, Po-Jui, e 陳柏睿. "Development of Automatic Fingerprint Verification Systems". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/21074703439603445585.
Testo completoAbstract (sommario):
碩士國立臺灣大學
機械工程學研究所
92
Most fingerprint verification systems take advantage of fingerprint minutiae as matching features. Since the classification rate of the minutia-based method is determined by the quality of input fingerprint images, the minutia-based method usually demands a large amount of image preprocessing to remove signal noise. Obviously, this not only increases system computation complexity, but also reduces matching speed. In order to avoid these drawbacks, this thesis combines the automatic threshold selection with differential pulse transform algorithm and adopts the wavelet transform to transfer a fingerprint signal from the spatial domain to the frequency domain. The magnitudes of the fingerprint energies, which distribute over different frequencies, are taken as fingerprint features for identification or verification, to reduce the computation load in the system. As for low quality fingerprint images, we join the wavelet transform and Gabor filter to enhance and restore crumbling segments. In addition, we use the registration point and rotation point as auxiliary reference to solve for the problem of frequency characteristic variations induced by fingerprint translation or rotation. With back propagation neural network as classifier, experiment shows that the classification rate can achieve 93% above in our system. The same result can be obtained for the fingerprint images in varied position or with poor quality.
25
Hsiao, Chih-Hao, e 蕭志豪. "Fingerprint Recognition Chip Design via the Improved Multi-level Fingerprint Verification Algorithm". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/s93sb7.
Testo completoAbstract (sommario):
碩士淡江大學
電機工程學系碩士班
94
Nowadays, the importance of the information protection and the entrance guard control system have be attached importance to the people. Traditional secret maintaining methods like setting up passwords are eliminated gradually, and the biometric verification technique is to take it over. Biometric verification technique is applied on enhancing the system of maintaining secrecy gradually. Biometric verification techniques which are available now are mainly as follows : iris, voice, appearance, and fingerprint. On account of its precision and convenience, fingerprint is the most wildly-used technique; whereas iris verification may cause transmitted disease because of the contact with eyes, voice may inflect for emotion, appearance maybe changed as weight. What’s more, fingerprints possess exclusive character, therefore, fingerprints have been using on information security system generally. Most of the fingerprint verification systems would including normalization, noise filter, calculate fingerprint flow direction, flow filter, thinning, detect the positions of the singular points and minutiae, as a basis for the later verification. However, the verification efficiency of a huge recognition database is often censorable when encountering. Therefore, this thesis brings up a new verification process to promote the efficiency of verification. After a series of valid image preprocessing procedure, we detect the minutiae without Thinning, and use the flow nearing the core point to classify the fingerprints speedily. As for the verification unit, the new verification process would verify the fingerprints of its classified database first, which will improve the verification efficiency of the whole verification unit. After the confirmation of algorithm and the success of simulation, we try to realize the procedure of fingerprint image verification by hardware, using DSP to achieve personal identity verification system.
26
Lin, Jeng-Je, e 林正哲. "Fingerprint Verification System using Phase Correlation Technique". Thesis, 2000. http://ndltd.ncl.edu.tw/handle/61344414746658609742.
Testo completoAbstract (sommario):
碩士國立臺灣師範大學
工業教育研究所
88
Abstract The technique of digital fingerprint matching has been used for more than thirty years. Two main traditional approaches to do fingerprint matching are minutiae-based matching and filter-based matching. They were proposed to extract the features of ridges and valleys pattern and verify the incoming fingerprint with stored fingerprint database fast and accurately. But the widely used minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. And the filter-based matching suffered much in false acceptance rate when dealt with defective fingerprints. The proposed phase-based matching algorithm uses phase difference to do fingerprint verification instead of traditional Euclidean distance. The amplitude value is normalized into unitary value. This robust phase-matching algorithm speeds up the fingerprint matching and reduces false acceptance rate after several experiments.
27
Li-Ming, Lee. "Development of an Anti-Forgery Fingerprint Verification System". 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2307200519203300.
Testo completo
28
Gwo-Cheng, Chao, e 趙國成. "A Fingerprint Verification System based on Minutiae Matching". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/69420330587510232317.
Testo completoAbstract (sommario):
碩士國立臺灣科技大學
資訊工程系
93
Fingerprint based identification has been known and used for a long time. Owing to the uniqueness and immutability, fingerprints are today the most widely used biometric features. Fingerprint verification is based on the minutiae of the fingerprints. Extracting minutiae from fingerprint patterns is one of the most important steps in automatic system for fingerprint identification. This model, adopted in most automatic systems, is based on a two-class minutiae classification: endpoint and bifurcation .The accuracy of the minutiae will affect the correctness of the fingerprint matching; therefore the fingerprint enhancement will be done to reduce the false minutiae. In this thesis, an effective fingerprint verification system is implemented by passing the image pre/post-processing, feature extraction and matching processes. We describe an improved method on fingerprint enhancement and post-processing. We directly enhance the fingerprint on gray-scale image and reduce the most false minutiae in the post-processing step. For minutiae matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the corresponding point pairs between input and template minutiae. It also has the ability to adaptively compensate for the nonlinear deformations between fingerprints.
29
Lee, Li-Ming, e 李立明. "Development of an Anti-Forgery Fingerprint Verification System". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/86092650347705322132.
Testo completoAbstract (sommario):
碩士國立臺灣大學
機械工程學研究所
93
In general, the fingerprint verification systems are good enough for daily secure life. But if the biometric technology of duplicating fingerprints has a significant improvement, or a bandit cuts off the authorizer’s finger, the only fingerprint verification procedure is not safe any more. Therefore, we have to develop an Anti-Forgery Fingerprint Verification System to ensure the fingerprint pattern directly extracting from one’s finger but not a duplicated fingerprint mask. An AFFVS (Anti-Forgery Fingerprint Verifications System) is a verification system combined with the biometric indicators of both fingerprint and finger-vein patterns. When a user places his finger on the sensor, the system will first check the primal feature, fingerprint pattern, to ensure that the input fingerprint pattern belongs to a authorized user, and then AFFVS will compare the input finger-vein pattern with the authorized user’s finger-vein pattern to guarantee the captured fingerprint is really acquired from the authorized person rather than the forgery fingerprint mask. But if the AFFVS decides the input fingerprint pattern as an invader, it will directly reject the user and will not do any vein-comparing procedure.
30
Tseng, Chun-Chieh, e 曾俊傑. "Design Methodology of An Intelligent Fingerprint Verification System". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/90582948099717557897.
Testo completoAbstract (sommario):
碩士義守大學
電機工程學系
89
This thesis advances a complete methodology of an intelligent fingerprint verification system. It applies circular auto-correlation to perform the features transformed from fingerprint images. SOM-BK neural network is used for extracting fingerprint minutia. An intelligent minutia point matching method based on particle swarm optimization is also applied. Circular auto-correlation produces a set of rotation-invariant feature vectors by transforming each local pattern on the fingerprint. We establish a mixed neural networks which combine SOM and MFNN neural networks to detect minutia from fingerprint images. In verification stage, we use PSO to optimize minutia point matching distortions including displacement and rotation. To compare with other methods, the searching of PSO is faster with higher adaptability. When PSO finds the optimal solution on the finite solution space, we can easily identify the target fingerprint image. According to this methodology, we design a fingerprint verification system that integrates software and hardware. This system is consisted of four main software modules which are circular auto-correlation module, SOM-BK feature extraction module, particle swarm optimization module, and decision maker module. Finally we test and adjust this system with real world data. The results reveal the expected performance and applicability of the system. They prove as well the availability of design methodology proposed in this thesis.
31
"The statistical evaluation of minutiae-based automatic fingerprint verification systems". Thesis, 2006. http://library.cuhk.edu.hk/record=b6074180.
Testo completoAbstract (sommario):
Basic technologies for fingerprint feature extraction and matching have been improved to such a stage that they can be embedded into commercial Automatic Fingerprint Verification Systems (AFVSs). However, the reliability of AFVSs has kept attracting concerns from the society since AFVSs do fail occasionally due to difficulties like problematic fingers, changing environments, and malicious attacks. Furthermore, the absence of a solid theoretical foundation for evaluating AFVSs prevents these failures from been predicted and evaluated. Under the traditional empirical AFVS evaluation framework, repeated verification experiments, which can be very time consuming, have to be performed to test whether an update to an AFVS can really lead to an upgrade in its performance. Also, empirical verification results are often unable to provide deeper understanding of AFVSs. To solve these problems, we propose a novel statistical evaluation model for minutiae-based AFVSs based on the understanding of fingerprint minutiae patterns. This model can predict the verification performance metrics as well as their confidence intervals. The analytical power of our evaluation model, which makes it superior to empirical evaluation methods, can assist system developers to upgrade their AFVSs purposefully. Also, our model can facilitate the theoretical analysis of the advantages and disadvantages of various fingerprint verification techniques. We verify our claims through different and extensive experiments.Chen, Jiansheng.
"November 2006."
Adviser: Yiu-Sang Moon.
Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5343.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (p. 110-122).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts in English and Chinese.
School code: 1307.
32
Hsieh, Fang-Ling, e 謝芳伶. "The Application of Fingerprint Verification based on Support Vector Machines". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/49dsxu.
Testo completoAbstract (sommario):
碩士靜宜大學
資訊管理學系研究所
92
The issues of security are broadly discussed nowadays. Without effective security protection, even the most excellent information system is still useless. So far there have been a variety of biological features for personal identification, including appearance, hand geometry, voice print, iris, retina, signature, facial thermogram, hand vein, gait, odor, keystroke, and so on . Of these various methods of protection, fingerprinting is one of the most common approaches. In this thesis, the mechanism of a novel fingerprint verification will be studied. The algorithm consists three parts, namely, the first part acquisition of fingerprint image, the acquest of features and the comparison of features. The contains the acquirement of image ridge. To detect the direction of fingerprint area, this study adopts the methods developed by Stock and Swonger to search for the whole picture of fingerprint’s flowing direction, and then present it with eight directions. The second part is based on the location of the central point. Then, the features are found according to the central points. This study uses one-class support vector machines(SVM) to distinguish fingerprints in the third part. Experiment results show that one-class SVM can apply to fingerprint recognition problem efficiently.
33
Lin, tun-hua, e 林敦華. "Fingerprint verification system apply to finance corporation－for customer relationship example". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/97410444926018213413.
Testo completoAbstract (sommario):
碩士國立臺北大學
企業管理學系碩士在職專班
92
By consumer psychology theory, scholars study and predict customer’s behavior based on “ cognition-attitude ” model. (Here in this research is about feasibility of consumer acceptation on fingerprint Verification system by Finance Corporations) In this research, is based on survey research to analyze consumer “ cognition-attitude ” and estimate feasibility of consumer acceptation on fingerprint Verification system by Finance Corporations. In a word, （1）Gender, age and career are all significantly connected with customer cognition based on statistic. （2） Gender, age and career are all significantly connected with customer attitude based on statistic. （3）Customer cognition is significantly connected with customer aggregative attitude based on statistic.（4）Customer attitude is significantly connected with customer aggregative attitude based on statistic.（5）Race attribute variability is significantly connected with customer aggregative attitude based on statistic. This research includes all dependent variability and influenced independent variability. Only eight variables is significantly influenced based on statistic: aware fingerprint could be recognized by computer scan(cognition)、not aware fingerprint could be recognized by computer scan(cognition)、Middle grounders towards privacy or/and corporation advantage(attitude)、privacy is more important(attitude)、extremely agreeable on fingerprint verification(attitude)、disapproval on fingerprint verification(attitude) 、extremely disapprove on fingerprint verification(attitude)、female(Race attribute)。 Above shows customer cognition, attitude and race attribute variability are all significantly connected with customer general attitude, which needs special attention while used on fingerprint verification system by finance corporations.
34
Lee, Chih-Jen, e 李志仁. "Applications of Gabor Functions on Fingerprint Representation, Enhancement, Verification, and Identification". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/78227525989803394566.
Testo completoAbstract (sommario):
博士國立臺灣大學
電機工程學研究所
89
This thesis describes how to take advantage of Gabor filters to the applications of fingerprint, such as enhancement, singular point detection, pattern extraction, verification, and identification. Based on the global and local information, we develop two kinds of Gabor filter-based approaches to extract the fingerprint features. To entirely analyze the influence of the parameters of Gabor filters for the global approach, we transform a local fingerprint image into a complete Gabor basis functions (GBFs) and find that the radial frequency is the most important parameter for fingerprint recognition. Moreover, we also find that the principal GBF, which has the maximum response over the complete GBFs, is sufficient to represent the ridge orientation and frequency of a local fingerprint image and the corresponding response can be used as an index of both the clearness of ridge structures and the area of fingerprint pattern. According to the ridge structures of the core region or those of whole fingerprint, we extract a set of principal GBFs to form a template. Then the core point detection and fingerprint matching can be performed directly from gray-scale image based on the largest responses. We also develop a fingerprint enhancement algorithm with adaptive Gabor filters to obtain very clear ridge structures. At last, we evaluate the performances of the global and the local Gabor filter-based features by various experiments of various system designs, such as verification and identification. Through various comparisons, in conclusion, the local approach is superior to the global from all situations, including accuracy, speed, and memory space.
35
Huang, Yi-Hong, e 黃逸泓. "A Rotation Invariance Singular Point Detection Algorithm on a Fingerprint Verification System". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/95355181004209072891.
Testo completoAbstract (sommario):
碩士清雲科技大學
電機工程研究所
95
A robust fingerprint singular point detection algorithm with rotation-invariant ability is proposed. Two 7x7 templates of core and delta are constructed from the orientation map of a fingerprint that is formed by using Sobel operators. The differences between the value of central element and those of the outer ring’s 24 elements of the template are calculated. Since the variations of the orientations around the singular point are greater than those of others, singular point can be correctly detected by comparing the 24 database . The algorithm has the merit to detect rotated fingerprint images that cannot be done by using traditional sample-matching method with only two templates. Experimental results show the correctness of our algorithm. Comparisons with the traditional sample-matching method are studied. Analyzing of detection performance by using different sample sizes is also performed.
36
Yu, Hsin-Wei, e 余信緯. "The Low Cost and Small Size Fingerprint Verification System Base on Line Sensor". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/72467p.
Testo completoAbstract (sommario):
碩士國立臺北科技大學
電腦與通訊研究所
94
While the diversity of the technology has been presented and advanced nowadays, it is time to enjoy the convenience from the progress of technology. Thus, the way to secure the personal data has been a more and more important issue. Traditionally, products related to electronics adopt the password inputting as the protection lock. But, the password is inconvenient since it acquires the user to memorize and it can easily be stolen. In the present study, a fingerprint verification system is the main focus in which the line sensor has been adopted to be the protection lock. The line sensor used here, requiring lower power, smaller area, and lower price, is different from the traditional block sensor, for the current system of science and technology on the market. This kind of sensor will be much suited for the product development. Because of the line sensor, the way to detect is to sweep and pick up the fingerprint, and, thus, the image that have already picked can be further reorganized to become as a full fingerprint image. Because the head fingerprint sweep on the sensor, having problems such as sweep speed, pressure differences, and work environment variables. The algorithm of backend expiation has to be applied to take for a further revision and for the adjustment of processing, to avoid problems that influence the recognized result. First, this present study adopts the Normalized Cross Correlation reorganization algorithm to recognize the complete fingerprint. Then, in order to enhance the function of a complete fingerprint image establishment, as the Histogram equalization, Gaussian smoothing, and image scaling, it is convenient to take its minutiae. We use the group delay spectrum algorithm to extract the fingerprint spectrum minutiae after the image has been enhanced. Finally, we use the dynamic programming algorithm to match and verify between fingerprint spectrum minutiae and the registered fingerprint, to complete the recognition. Because this system uses the spectrum minutiae of the group delay spectrum to be as the sample, it can be used to compare with the traditional endpoint and bifurcation minutiae. Since this system contains the advantage with lower calculation quantity and less memory, it requires much less calculation for the quantity of image front processing and image enhancement function. This system is based on a line sensor and group delay spectrum in which the relevant advantages have been mentioned above. Therefore, through the experiment, our recognition rate is up 93.82%.
37
Huang, Yi-Che, e 黃一哲. "Fingerprint Recognition Chip Design via the Fast Minutiae Extraction and Multi-level Verification Algorithm". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/56436125310223209108.
Testo completoAbstract (sommario):
碩士淡江大學
電機工程學系碩士班
95
Nowadays, the importance of the information protection and the entrance guard control system are quite important lessons. Today, biometric verification technique is applied on enhancing the system of maintaining secrecy and recognized identifications. Biometric verification techniques which are available now are mainly as follows: voice, appearance, iris, fingerprint. Fingerprint is so authentic adopted for people constantly because of its singularity and constancy. Most identification methods would including normalization, noise filter, calculate fingerprint flow direction, flow filter, thinning, detect the positions of the singular points and minutiae. The primal methods are depend on the locations and numbers of ridge endings and bifurcations. If we can get the fingerprint flow field exactly, it will be easy to find out the minutiae and singular points. In this thesis, we propose a novel improved binarization to replace gabor filter. This method can accelerate the speed of the calculation of fingerprint flow and help us gain the flow more accurately. We also advance a new verification process to promote the efficiency of verification. After a series of valid image preprocessing procedure, we detect the minutiae without Thinning, and use the flow nearing the core point to classify the fingerprints speedily. As for the verification unit, the new verification process would verify the fingerprints of its classified database first, which will improve the verification efficiency of the whole verification unit.
38
(9183044), Samuel J. Reiff. "Analysis of Fingerprint Recognition Performance on Infants". Thesis, 2020.
Cerca il testo completoAbstract (sommario):
In this study, any change in fingerprint performance, image quality and minutiae count for infants in three different age groups was evaluated (0-6, 7-12, and >12 months). This was done to determine whether there is a difference in performance between infant age groups for a fingerprint recognition system.
The purpose of this research was to determine whether there is a difference in infant fingerprint performance and image quality metrics, between three different age groups (0-6, 7-12, and >12 months old), using the same optical sensor? The data used for this secondary analysis was collected as part of a longitudinal multimodal infant study, using the Digital Persona U.are.U 4500. DET curves, zoo analysis, and image quality metrics were used to evaluate performance and quality factored by infant age group.
This study found that there was a difference in image quality and minutiae count, genuine and impostor match scores, and performance error rates (EER) between the three age groups. Therefore, quality and performance were dependent on age. While there was a difference in performance between age groups, there was generally stability for subjects who overlapped between multiple age groups. Difference in performance was most likely due to the difference in physical characteristics between subjects in each age group, rather than individual instability. The results showed that it could potentially be feasible to use fingerprint recognition for children over the age of 12 months.
39
Braude, David Adam. "Fingerprinnts: oriantation free minutiae extraction and using distances between minutiae for identification and verification". Thesis, 2011. http://hdl.handle.net/10539/10623.
Testo completoAbstract (sommario):
Fingerprint recognition has become a standard in both access control and forensics.
This is because fingerprints are unique to an individual. While there are many
ways in which a fingerprint can be recognised one of the most common is to look
at the endings and splitting of the ridges. These are called minutiae. This research
undertakes to improve upon existing methods used in all parts of a minutiae based
fingerprint verification system. This study aims to find a new way to extract these
minutiae. It also seeks to use them in a novel way to identify an individual and
verify that two fingerprints come from the same person. This was done in an
effort to improve speed in fingerprint recognition systems by reducing the processing
overhead.
There is one key difference between the new extraction algorithm and standard
methods. In the new method for extraction the orientation of the ridges is not found.
This was done to speed the process of extraction. To verify that two fingerprints are
the same the distances between minutiae was considered to be binary attributes of
a graph. This turned the verification into a graph-matching problem. The distances
between the minutiae were split into a histogram and the values in the bins were the
inputs to a Multilayer Perceptron (MLP). This MLP was used to group fingerprints
into classes to speed the identification process. The MLP was trained using Particle
Swarm Optimisation.
The new extraction algorithm finds minutiae very quickly. However, it finds many false minutiae. The graph-matching approach is unable to distinguish between a
match and a non-match and is very slow to run. This is also true for the case when
the unary attributes are included. These attributes are the type of minutiae and
angle of the ridge at the minutiae point. The classifier runs quickly, but places all
the fingerprints in the same class. Thus it will not improve identification time.
It is possible that a filtering system could be developed to combat the amount of
false minutiae. This would make the new algorithm viable. Care must be taken to avoid increasing the runtime to beyond industry standard. The amount of spurious
minutiae could be affecting the performance of the graph matching and classification.
Alternatively it could be due to different minutiae being extracted between scans.
This is due to different parts of the finger are observed with each scan. The cause
will need to be investigated.
While positive results were not obtained, this research forms the basis of future
investigation. Two questions will now need to be answered. The first is can a filter
be developed to remove spurious minutiae from the extraction process? The second,
are the spurious minutiae the cause of the problem or will only using the distances
be sufficient? If the latter is the case, then finding the angle of the ridge is no longer
necessary and the minutiae extraction process can be speeded up by using the new
algorithm.