Indexing techniques for fingerprint and iris databases

This thesis addresses the problem of biometric indexing in the context of fingerprint and iris databases. In large scale authentication system, the goal is to determine the identity of a subject from a large set of identities. Indexing is a technique to reduce the number of candidate identities to be considered by the identification algorithm. The fingerprint indexing technique (for closed set identification) proposed in this thesis is based on a combination of minutiae and ridge features. Experiments conducted on the FVC2002 and FVC2004 databases indicate that the inclusion of ridge features aids in enhancing indexing performance. The thesis also proposes three techniques for iris indexing (for closed set identification). The first technique is based on iriscodes. The second technique utilizes local binary patterns in the iris texture. The third technique analyzes the iris texture based on a pixel-level difference histogram. The ability to perform indexing at the texture level avoids the computational complexity involved in encoding and is, therefore, more attractive for iris indexing. Experiments on the CASIA 3.0 database suggest the potential of these schemes to index large-scale iris databases

Image Processing and Features Extraction of Fingerprint Images

Several fingerprint matching algorithms have been developed for minutiae or template matching of fingerprint templates. The efficiency of these fingerprint matching algorithms depends on the success of the image processing and features extraction steps employed. Fingerprint image processing and analysis is hence an essential step to the efficient matching and classification of fingerprint features. To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of the features extraction of the datasets of the FVC 2000 database. It also discusses the limitations of the FVC database and recommends what can be done to improve proprietary databases

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

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

Low-Quality Fingerprint Classification

Traditsioonilised sõrmejälgede tuvastamise süsteemid kasutavad otsuste tegemisel minutiae punktide informatsiooni. Nagu selgub paljude varasemate tööde põhjal, ei ole sõrmejälgede pildid mitte alati piisava kvaliteediga, et neid saaks kasutada automaatsetes sõrmejäljetuvastuse süsteemides. Selle takistuse ületamiseks keskendub magistritöö väga madala kvaliteediga sõrmejälgede piltide tuvastusele – sellistel piltidel on mitmed üldteada moonutused, nagu kuivus, märgus, füüsiline vigastatus, punktide olemasolu ja hägusus. Töö eesmärk on välja töötada efektiivne ja kõrge täpsusega sügaval närvivõrgul põhinev algoritm, mis tunneb sõrmejälje ära selliselt madala kvaliteediga pildilt. Eksperimentaalsed katsed sügavõppepõhise meetodiga näitavad kõrget tulemuslikkust ja robustsust, olles rakendatud praktikast kogutud madala kvaliteediga sõrmejälgede andmebaasil. VGG16 baseeruv sügavõppe närvivõrk saavutas kõrgeima tulemuslikkuse kuivade (93%) ja madalaima tulemuslikkuse häguste (84%) piltide klassifitseerimisel.Fingerprint recognition systems mainly use minutiae points information. As shown in many previous research works, fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this challenge, in this thesis, we are focusing on very low-quality fingerprint images, which contain several well-known distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. We develop an efficient, with high accuracy, deep neural network algorithm, which recognizes such low-quality fingerprints. The experimental results have been conducted on real low-quality fingerprint database, and the achieved results show the high performance and robustness of the introduced deep network technique. The VGG16 based deep network achieves the highest performance of 93% for dry and the lowest of 84% for blurred fingerprint classes

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

SMART TECHNIQUES FOR FAST MEDICAL IMAGE ANALYSIS AND PROCESSING

Medical Imaging has become an important transversal applications and re- search field that embraces a great variety of sciences. Imaging is the central science of measurement in diagnosis and treating diseases. The effort of the technological progress has made possible human imaging starting from a single molecule to the whole body. The open challenge is to treat the huge amount of medical informations with the use of smart and fast techniques that allows clinical and images data analysis and processing. In this ph.D. Thesis, many issues have been addressed and a certain amount of improvement in various fields have been produced, such as biom- etry, organs and tissues segmentation, MRI thermometry, medical reports retrieval and classification. The topic prefixed at the beginning of this ph.D. route was to analyze, understand, and give a step over to various kind of problematics related to Medical Images and Data analysis, working closely to radiologist physicians, with specific equipments, and following the common denominator of fast and smart methodologies applied to the medical imaging issue. A series of contribution have been carried out in fields such as: • proposing two different kind of multimodal biometric authentication systems that investigates fingerprint and iris fusion and processing; • applying expert systems to the issue of data validation, comparing and validating data to two different methodologies that assess liver iron overload in thalassemic patients;• addressing and improving non-invasive referenceless thermometry by using Radial Basis Function as interpolator; • applying the multi-seed region growing method to the segmentation of CT liver dataset; • proposing a novel unsupervised voxel-based morphology method for MRI brain segmentation by using k-means clustering and neural net- work classification; • proposing a novel ontology-based algorithm for information retrieval from mammographic text reports. The above work has been developed with the cooperation of the medical staff of the “Dipartimento di Biopatologia e Biotecnologie Mediche e Forensi” and the “Scuola di Specializzazione in Radiodiagnostica" of the Università degli Studi di Palermo. All the proposed contributions show good performance using the stan- dard metrics. Most of them have produced scientific publications in com- puter science venues as well as in radiological venues. In addition, some specific frameworks, such as OsiriX, have been used to improve usability and easiness of the developed systems