A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management and personal ubiquitous authentication. The multimodal technique uses both fingerprint micro features (minutiae) and fingerprint macro features (singularity points) for robust user authentication. To strengthen the security level of electronic payment systems, an embedded hardware prototype has been also created: acting as self-contained sensors, it performs the entire authentication process on the same device, so that all critical information (e.g. biometric data, account transactions and cryptographic keys), are managed and stored inside the sensor, without any data transmission. The sensor has been prototyped using the Celoxica RC203E board, achieving fast execution time, low working frequency, and good recognition performance

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Facilitating sensor interoperability and incorporating quality in fingerprint matching systems

This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database

Skeleton-based fingerprint minutiae extraction.

by Zhao Feng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 64-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.viTable of Contents --- p.viiList of Figures --- p.ixList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic Personal Identification --- p.1Chapter 1.2 --- Biometrics --- p.2Chapter 1.2.1 --- Objectives --- p.2Chapter 1.2.2 --- Operational Mode --- p.3Chapter 1.2.3 --- Requirements --- p.3Chapter 1.2.4 --- Performance Evaluation --- p.4Chapter 1.2.5 --- Biometric Technologies --- p.4Chapter 1.3 --- Fingerprint --- p.6Chapter 1.3.1 --- Applications --- p.6Chapter 1.3.2 --- Advantages of Fingerprint Identification --- p.7Chapter 1.3.3 --- Permanence and Uniqueness --- p.8Chapter 1.4 --- Thesis Overview --- p.8Chapter 1.5 --- Summary --- p.9Chapter Chapter 2 --- Fingerprint Identification --- p.10Chapter 2.1 --- History of Fingerprints --- p.10Chapter 2.2 --- AFIS Architecture --- p.12Chapter 2.3 --- Fingerprint Acquisition --- p.15Chapter 2.4 --- Fingerprint Representation --- p.16Chapter 2.5 --- Fingerprint Classification --- p.18Chapter 2.6 --- Fingerprint Matching --- p.20Chapter 2.7 --- Challenges --- p.21Chapter 2.8 --- Combination Schemes --- p.22Chapter 2.9 --- Summary --- p.23Chapter Chapter 3 --- Live-Scan Fingerprint Database --- p.24Chapter 3.1 --- Live-Scan Fingerprint Sensors --- p.24Chapter 3.2 --- Database Features --- p.24Chapter 3.3 --- Filename Description --- p.28Chapter Chapter 4 --- Preprocessing for Skeleton-Based Minutiae Extraction --- p.30Chapter 4.1 --- Review of Minutiae-based Methods --- p.31Chapter 4.2 --- Skeleton-based Minutiae Extraction --- p.32Chapter 4.2.1 --- Preprocessing --- p.33Chapter 4.2.2 --- Validation of Bug Pixels and Minutiae Extraction --- p.40Chapter 4.3 --- Experimental Results --- p.42Chapter 4.4 --- Summary --- p.44Chapter Chapter 5 --- Post-Processing --- p.46Chapter 5.1 --- Review of Post-Processing Methods --- p.46Chapter 5.2 --- Post-Processing Algorithms --- p.47Chapter 5.2.1 --- H-Point --- p.47Chapter 5.2.2 --- Termination/Bifurcation Duality --- p.48Chapter 5.2.3 --- Post-Processing Procedure --- p.49Chapter 5.3 --- Experimental Results --- p.52Chapter 5.4 --- Summary --- p.54Chapter Chapter 6 --- Conclusions and Future Work --- p.58Chapter 6.1 --- Conclusions --- p.58Chapter 6.2 --- Problems and Future Works --- p.59Chapter 6.2.1 --- Problem 1 --- p.59Chapter 6.2.2 --- Problem 2 --- p.61Chapter 6.2.3 --- Problem 3 --- p.61Chapter 6.2.4 --- Future Works --- p.62Bibliography --- p.6

Embedded Biometric Sensor Devices: Design and Implementation on Field Programmable Gate Array

During the research activity in my Ph.D. course, I thoroughly studied the biometric systems and the relevant design and implementation techniques allowing the employment of such systems in embedded devices. I focused my attention on the fingerprint-based recognition and classification systems, and on their implementation on Field Programmable Gate Array (FPGA) devices. I was prompted to study biometric systems mainly because these systems may play a key role in the new emerging market of mobile devices (for example, they are recently available in the new generation of Apple and Samsung smart phones). Such market is rapidly growing and influencing the way people use network resources and functionalities (such as commercial, banking, and government services), requiring a security level higher than in the past. Consequently, novel design techniques and technologies for user recognition and are required to be investigated, in order to provide a secure services and resources access. The traditional authentication systems based on username and password are not able to guarantee a suitable protection level. Unlike password, instead, user biometric information is unique and unchangeable; therefore the biometric identity has the advantage to guarantee that only the authorized users have access to available resources and services. However, traditional biometric approaches involves interactions among a large number of entities: passive access points for user biometric trait acquisition, networked databases for user biometric identity storing, and trusted servers running the user recognition systems. So, traditional systems usually undergo several types of attacks, such as Communication Attack (attacking the channel between the server and the database), Replay Attack (replication of user biometric trait processed during the acquisition phase), and Database Attack (manipulation of the stored user biometric identity). Embedded architectures, instead, provide a more secure and flexible infrastructure, since all elaboration steps are performed on board, so biometric identities are securely managed and stored inside the system without any data leaking out. The goal of this thesis is to illustrate the analysis and results of my research activity focused on the design and development of new fingerprint-based recognition systems for embedded devices. The study of the state-of-the-art about biometric systems led me to realize novel approaches to improve the performance of standard systems in order to enable their employment in embedded devices architectures. Most common literature approaches used to implement fingerprint-based recognition and classification systems are reported to provide a starting-point for understanding the contribution of this work. There are many literature approaches to deal with software systems, but few on design and implementation of embedded hardware prototypes. Referring to the developed and proposed fingerprint-based systems, this thesis represents an advancement of embedded biometrics respect to state-of-the-art. The step-over proposed in this work is focused on: 1. a heuristic fingerprint classification technique, requiring only a little set of images as training dataset; 2. an advanced matching technique for personal recognition based on partial fingerprint, able to enhance the system accuracy; 3. the design and implementation of an efficient fingerprint features extractor; 4. the design and implementation of a quality evaluator of raw fingerprint images (able to identify poor quality areas, such as dry and moist portions), allowing to define a novel flow of image processing steps for user recognition. This thesis is divided into two parts, creating a path connecting the state-of-the-art about biometric systems and the novel implemented approaches. The knowledge of the state-of-the-art about biometrics is fundamental to understand the step over presented in this work. For this reason, in the first part, general characteristics of biometric systems are presented with particular reference to fingerprint-based approaches used in literature to realize embedded systems. The second part proposes the developed innovative sensor. A novel flow of image processing steps for user recognition is outlined. Successively, an efficient micro and macro fingerprint features extractor is illustrated. Then, an advanced matching technique for personal recognition using partial fingerprints is presented. Finally, an innovative fingerprint classification approach based on the fusion of Fuzzy C-Means and Naive-Bayes technique is detailed. Experimental results and comparisons with analogous literature systems show the effectiveness on the proposed sensor. All the innovative approaches proposed in this thesis have been published in international conferences and journals