Security is very important in present day life. In this highly-interconnected world, most of our daily activities are computer based, and the data transactions are protected by passwords. These passwords identify various entities such as bank accounts, mobile phones, etc. People might reuse the same password, or passwords related to an individual that can lead to attacks. Indeed, remembering several passwords can become a tedious task. Biometrics is a science that measures an individual’s physical characteristics in a unique way. Thus, biometrics serves as a method to replace the cumbersome use of complex passwords. Our research uses the features of biometrics to efficiently implement a biometric encryption system with a high level of security.

<p>Verification of users’ identities are normally carried out via PIN-codes or ID- cards. Biometric identification, identification of unique body features, offers an alternative solution to these methods. </p><p>Fingerprint scanning is the most common biometric identification method used today. It uses a simple and quick method of identification and has therefore been favored instead of other biometric identification methods such as retina scan or signature verification. </p><p>In this report biometric security systems have been evaluated based on fingerprint scanners. The evaluation method focuses on copies of real fingers, artificial fingers, as intrusion method but it also mentions currently used algorithms for identification and strengths and weaknesses in hardware solutions used. </p><p>The artificial fingers used in the evaluation were made of gelatin, as it resembles the surface of human skin in ways of moisture, electric resistance and texture. Artificial fingers were based on ten subjects whose real fingers and artificial counterpart were tested on three different fingerprint scanners. All scanners tested accepted artificial fingers as substitutes for real fingers. Results varied between users and scanners but the artificial fingers were accepted between about one forth and half of the times. </p><p>Techniques used in image enhancement, minutiae analysis and pattern matching are analyzed. Normalization, binarization, quality markup and low pass filtering are described within image enhancement. In minutiae analysis connectivity numbers, point identification and skeletonization (thinning algorithms) are analyzed. Within pattern matching, direction field analysis and principal component analysis are described. Finally combinations of both minutiae analysis and pattern matching, hybrid models, are mentioned. </p><p>Based on experiments made and analysis of used techniques a recommendation for future use and development of fingerprint scanners is made.</p>

Orientador: Aparecido Nilceu Marana / Banca: Fátima de Lourdes dos Santos Nunes Marques / Banca: Marcos Antônio Cavenaghi / Resumo: Biometria refere-se ao uso de características físicas (impressões digitais, íris, retina) ou comportamentais (assinatura, voz) para a identificação humana. As impressões digitais são formadas por cristas e minúcias. As cristas são linhas distribuídas paralelamente com uma orientação e um espaçamento característico e as minúcias representam os vários modos pelos quais uma crista pode se tornar descontínua. Graças a sua universalidade, unicidade e permanência, as impressões digitais tornaram-se as características biométricas mais amplamente utilizadas. Entretanto, considerar o reconhecimento automático de impressões digitais um problema totalmente resolvido é um erro muito comum. Nenhum sistema de reconhecimento de impressões digitais proposto até hoje é infalível, nenhum garante taxas de erro nulas. Imagens de baixa qualidade e com pequena área de sobreposição entre a imagem template e a imagem de consulta ainda representam um desafio para os métodos de reconhecimento de impressões digitais mais utilizados, os métodos baseados no casamento de pontos de minúcias. Uma das maneiras de superar as limitações e melhorar a acurácia de um sistema biométrico é o uso da multibiometria, isto é, a combinação de diferentes tipos de informação em um sistema de reconhecimento biométrico. Neste contexto, esta dissertação de mestrado objetiva aprimorar a acurácia dos sistemas de reconhecimento de impressões digitais por meio da fusão de métodos baseados em minúcias e em cristas. Para tanto, foram implementadas técnicas de fusão no nível de pontuação, classificação e decisão. No nível de pontuação, a fusão propiciou uma redução na taxa de erro igual (EER) de 42,53% em relação ao método mais preciso. Para o nível de classificação, a fusão significou um aumento de 75% na taxa de recuperação correta... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Biometrics refers to the use of physical (fingerprints, iris, retina) or behavioral (signature, voice) characteristics to determine the identity of a person. Fingerprints are formed by ridges and minutiae. The ridges are lines distributed in parallel with an orientation and a characteristic spacing and the minutiae represent the several ways a ridge can become discontinued. As to its universality, uniqueness and permanence, the fingerprints became the most widely used biometric characteristic. However, it is a common mistake to consider the automatic fingerprint recognition as a totally solved problem. No fingerprint recognition system proposed until now is infallible, none of them guarantee null error rates. Poor quality images and when just a small area of overlap between the template and the query images exists are still a complex challenge to the most used fingerprint recognition methods, the methods based on minutiae points matching. One of the possibilities to overcome the limitations and improve the accuracy of a biometric system is the use of multibiometrics, the combination of different kinds of information in a biometric system. In this context, this master thesis aims to improve the accuracy of fingerprint recognition systems through the fusion of minutiae based and ridge based methods. To achieve this, fusion techniques on score, rank and decision levels were implemented. For the score level, the fusion lead to a reduction of the Equal Error Rate to 42.53% compared to the most precise method. For the rank level, the fusion meant an increase of 75% in the Correct Retrieval Rate. And, in the decision level fusion the Recognition Rate changed from 99.25% to 99.75%. The results have demonstrated that the fusion of minutiae based and ridge based methods can represent a significant accuracy improvement for the fingerprint recognition systems. / Mestre

The fingerprints are lines present on fingers of each human and they are unique, in other words, there are not two people with the same distribution of lines on their fingers. For this reason, it has been used for identification of people for many purposes, such as to check the entrancy and exit of workers in a company, the identify criminals and to restrict the access of people to restrict areas of extreme security. The proposal of this work is to improve the characteristic extraction algorithm of fingerprint developed by Jain and others. The goal of this work is to accelerate the processing time in orientation field estimation, in order to get a better quality of images, resulting of binarization process and to decrease the number of spurious minutiae on the images. The time processing of orientation field estimation improved in these work uses the commutative propriety is smaller than the same estimation wich does not use this propriety in 90%. The method of ridges extration developed in this work uses the DDA algorithm, and results in better quality images. Due to this improviment got in binarization process and the ridges break removing, the detection minutiae algorithm implemented in this work find a small quantity of spurious minutae present in the fingerprint images. / As impressões digitais são as linhas presentes nos dedos de cada ser humano e essas são únicas para cada um, ou seja, não existem duas pessoas que possuem a mesma forma com que as linhas da impressão se dispõem em seus dedos. Por esse motivo é que ela vem sendo utilizada para a identificação de pessoas para várias finalidades, como por exemplo, o controle de ponto dos funcionários de uma empresa, a identificação de um criminoso e o acesso de pessoas a áreas restritas de extrema segurança. A proposta deste trabalho é obter melhoramentos no algoritmo de extração de características de impressões digitais desenvolvido por Jain e outros [1]. Os objetivos desta dissertação são: melhorar o tempo de processamento no cálculo da orientação de campo, obter uma melhor qualidade das imagens resultantes no processo de binarização e diminuir o número de minúcias espúrias nessas imagens. O cálculo da orientação de campo aperfeiçoado neste trabalho utiliza a propriedade da comutação e resultou em uma redução no tempo de processamento em torno de 90% em relação ao mesmo cálculo que não utiliza essa propriedade. O método de extração das saliências desenvolvido nesta dissertação utiliza o algoritmo DDA, e resulta em imagens de melhor qualidade. Devido a esse melhoramento obtido no processo de binarização e a remoção de falhas nas saliências, o algoritmo de detecção de minúcias implementado neste trabalho encontra uma menor quantidade de minúcias espúrias presentes nas imagens de impressão digital. / Mestre em Ciências

Verification of users’ identities are normally carried out via PIN-codes or ID- cards. Biometric identification, identification of unique body features, offers an alternative solution to these methods. Fingerprint scanning is the most common biometric identification method used today. It uses a simple and quick method of identification and has therefore been favored instead of other biometric identification methods such as retina scan or signature verification. In this report biometric security systems have been evaluated based on fingerprint scanners. The evaluation method focuses on copies of real fingers, artificial fingers, as intrusion method but it also mentions currently used algorithms for identification and strengths and weaknesses in hardware solutions used. The artificial fingers used in the evaluation were made of gelatin, as it resembles the surface of human skin in ways of moisture, electric resistance and texture. Artificial fingers were based on ten subjects whose real fingers and artificial counterpart were tested on three different fingerprint scanners. All scanners tested accepted artificial fingers as substitutes for real fingers. Results varied between users and scanners but the artificial fingers were accepted between about one forth and half of the times. Techniques used in image enhancement, minutiae analysis and pattern matching are analyzed. Normalization, binarization, quality markup and low pass filtering are described within image enhancement. In minutiae analysis connectivity numbers, point identification and skeletonization (thinning algorithms) are analyzed. Within pattern matching, direction field analysis and principal component analysis are described. Finally combinations of both minutiae analysis and pattern matching, hybrid models, are mentioned. Based on experiments made and analysis of used techniques a recommendation for future use and development of fingerprint scanners is made.

The work deals with the resistance of the VeriFinger comparison algorithm to synthetic fingerprints. The basics of fingerprints, comparison algorithms and synthetic fingerprint generators are gradually analyzed. In total, three experiments were designed to test the resistance of the algorithm using synthetic fingerprints. An application using the VeriFinger algorithm was implemented to evaluate individual experiments. A total of 2 800 000 synthetic fingerprints were generated across all experiments. The first experiment showed that random fingerprint generation was not effective for real fingerprints, but was somewhat sufficient for synthetic fingerprints. The second experiment proved that comparison and the comparison score could be used to estimate the class of the identified fingerprint, and in the third experiment, by narrowing the generation to one class, it raised the total number of matched fingerprints. The results of individual experiments were written and their possible extension was suggested.

Fingerprints are the oldest and most used form of biometric identification. A critical step is reliable extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality, they may be degraded and corrupted due to natural variations in skin and sensing conditions. Thus, image enhancement techniques are necessary prior to minutiae extraction. This work includes implementation of three techniques for fingerprint image enhancement, minutiae extraction and consturction of fingerprint reading device. Experiments are realized with two sets of fingerprints to evaluate the performance of implemented techniques.

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.

Η αυτόματη ταυτοποίηση εικόνων δακτυλικών αποτυπωμάτων αποτελεί ένα δύσκολο και πολυδιάστατο πρόβλημα, το οποίο έχει απασχολήσει πλήθος ερευνητών και για το οποίο έχει αναπτυχθεί μεγάλος αριθμός τεχνικών. Η δυσκολία του προβλήματος έγκειται στο γεγονός ότι οι εικόνες των αποτυπωμάτων είναι σε μεγάλο ποσοστό αλλοιωμένες ή ακόμα σε κάποιες περιπτώσεις δεν είναι διαθέσιμη η πλήρης εικόνα του αποτυπώματος, αλλά μόνο ένα μέρος αυτής. Στη συγκεκριμένη διατριβή, προτείνονται δύο μέθοδοι αυτόματης ταυτοποίησης δακτυλικών αποτυπωμάτων: α) η μέθοδος ταυτοποίησης δακτυλικών αποτυπωμάτων με χρήση τεχνικών ευθυγράμμισης και β) μέθοδος ταυτοποίησης δακτυλικών αποτυπωμάτων από το συνδυασμό του Δικτύου Αυτό-Οργανούμενων Δικτύων του Kohonen και του ορισμού των σημείων μικρολεπτομερειών των αποτυπωμάτων ως νευρώνων του δικτύου. Επιπλέον, ιδιαίτερη βαρύτητα δόθηκε στην προεπεξεργασία των εικόνων των δακτυλικών αποτυπωμάτων βάσει της ανάπτυξης και εφαρμογής κατάλληλων τεχνικών επεξεργασίας εικόνων προκειμένου να προκύψει βελτίωση της ποιότητας της εικόνας του δακτυλικού αποτυπώματος και να εξαχθούν οι μικρολεπτομέρειες που χρησιμοποιούνται για την ταύτιση των δακτυλικών αποτυπωμάτων. Στο πλαίσιο της παρούσας διατριβής, χρησιμοποιήθηκαν δεδομένα δακτυλικών αποτυπωμάτων από τις βάσεις VeriFingerSample_DB της Neurotechnology και η DB3 του διαγωνισμού δακτυλικών αποτυπωμάτων FVC2004. Για την ποσοτική αποτίμηση της απόδοσης των προτεινόμενων μεθόδων χρησιμοποιήθηκε το κριτήριο της Αναλογίας Ίσου Σφάλματος (EqualErrorRate – EER). Σύμφωνα με το κριτήριο αυτό, η μέθοδος ταυτοποίησης δακτυλικών αποτυπωμάτων βάσει του Δικτύου Αυτό-Οργανούμενων Δικτύων παρείχε καλύτερα αποτελέσματα σε σύγκριση με οποιαδήποτε μέθοδο ευθυγράμμισης που εφαρμόστηκε. / Automatic Fingerprint Identification is a difficult and multidimensional problem. For this reason, the number of papers and techniques regarding this issue is numerous. The hardness of the problem lies with the fact that there is a large percentage of corrupted and partial fingerprint images. Throughout this Thesis, two methods were proposed for the Automatic Fingerprint Identification: a) the Automatic Fingerprint Identification based on registration techniques and b) the Automatic Fingerprint Identification based on the theory of Self Organizing Maps of Kohonen, setting the minutiae of the fingerprint images as input neurons of the Map. Furthermore, an important step prior to the application of the proposed automatic fingerprint identification methods is the pre-processing of these images by the development and implementation of a series of image processing techniques in order to enhance the image quality and to extract the minutiae which are then used for the fingerprint identification. In this Thesis, a substantial number of fingerprint images were used from the database VeriFingerSample_DB kai from the database DB3 of the competition FVC2004. The quantitative evaluation of both proposed automatic fingerprint identification methods were based on the Equal Error Rate (EER) criterion. According to this, the Automatic Fingerprint Identification based on the Self Organizing Maps outperformed against any other method based on registration techniques.

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.