• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 6
  • 4
  • 2
  • 1
  • 1
  • 1
  • Tagged with
  • 17
  • 17
  • 11
  • 5
  • 5
  • 4
  • 4
  • 4
  • 3
  • 3
  • 3
  • 3
  • 3
  • 2
  • 2
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Metodologia para extração de características invariantes à rotação em imagens de impressões digitais / Methodology for the extraction of features invariant to the rotation in fingerprint images

Mazetti, Cristina Mônica Dornelas 29 September 2006 (has links)
O objetivo deste trabalho é apresentar algoritmos aplicados para extração de características invariantes à rotação em imagens de impressões digitais. No pré-processamento da imagem utiliza-se detecção de bordas pelo detector de Canny tendo como resultado uma imagem binarizada e afinada. Na extração das minúcias a metodologia adotada é o número de cruzamentos (CN), que extrai os aspectos locais, tais como, as minúcias fim de linha e bifurcações. A direção das cristas locais não é utilizada porque nas imagens rotacionadas a condição de permanência das propriedades biométricas não são satisfeitas. A comparação das impressões digitais utiliza os vetores gerados pela extração de minúcias considerando a posição (x,y) da minúcia armazenada em um vetor por tipo de minúcia (um vetor para crista final e outro vetor para crista bifurcada) e calculando a distância Euclidiana dessa posição (x,y) ao centro de massa da distribuição de minúcias para cada tipo de minúcia. Assim, as duas imagens são similares quando a distância Euclidiana entre os vetores de cada imagem e por tipo de minúcia forem mínimas. São discutidas as limitações de outros trabalhos existentes envolvendo rotação, translação e distorção da imagem de impressão digital, mostrando que os poucos trabalhos que tratam o problema possuem resultados não satisfatórios. Os maiores problemas ocorridos foram a extração de minúcias espúrias, mas foram resolvidos com os métodos sugeridos por Dixon (1979), tendo resultados satisfatórios em duas metodologias. No método média, a precisão para encontrar uma imagem foi de 100%, duas imagens 97,32%, três imagens 92,35%, quatro imagens 86,41% e cinco imagens 71,86%. E no método normal, a precisão para encontrar uma imagem foi de 100%, duas imagens 99,20%, três imagens 96,95%, quatro imagens 94,00% e cinco imagens 76,43%. / The objective of this research is to present algorithms that can be applied in fingerprints images in order to extract certain features, which are invariant to an likely rotation in the given image. In the preprocessing stage, the Canny border detector is used, resulting in a binary, fine tuned image. For the minutiae extraction, the crossing number method is used, which extracts local aspects such as minutiae endings and bifurcations. The direction of local ridges is ignored because, in rotated images, the permanence conditions of the biometric attributes are not fulfilled. The process of matching fingerprints uses two arrays (one for ridge endings and the other for bifurcations), which are generated by the extraction of the minutiae, considering the (x,y) coordinate of the given minutiae stored in the arrays, and calculating its Euclidian distance relating to the center of mass of the minutiae distribution, for each of its types (ending or bifurcation). Thus, both images are similar when the Euclidian distance between the arrays of each image, distinct by the type of each minutiae, is minimal. The limitations of other pieces of research works concerning fingerprint image rotation, translation and distortion are discussed, indicating that the only few ones that deal with these kinds of problems give unsatisfactory results.
2

MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico / MFIS: algorithm for the recognition and indexing in database of fingerprints in metric spaces

Evandro de Araújo Jardini 31 August 2007 (has links)
O problema dos métodos tradicionais de identificação de pessoas é que são baseados em senhas e assim podem ser esquecidas, roubadas, perdidas, copiadas, armazenadas de maneira insegura e até utilizadas por uma pessoa que não tenha autorização. Os sistemas biométricos automáticos surgiram para oferecer uma alternativa para o reconhecimento de pessoas com maior segurança e eficiência. Uma das técnicas biométricas mais utilizadas é o reconhecimento de impressões digitais. Com o aumento do uso de impressões digitais nestes sistemas, houve o surgimento de grandes bancos de dados de impressões digitais, tornado-se um desafio encontrar a melhor e mais rápida maneira de recuperar informações. De acordo com os desafios apresentados, este trabalho tem duas propostas: i) desenvolver um novo algoritmo métrico para identificação de impressões digitais e ii) usá-lo para indexar um banco de dados de impressões digitais através de uma árvore de busca métrica. Para comprovar a eficiência do algoritmo desenvolvido foram realizados testes sobre duas bases de imagens de impressões digitais, disponibilizadas no evento Fingerprint Verification Competition dos anos de 2000 e 2002. Os resultados obtidos foram comparados com os resultados do algoritmo proposto por Bozorth. A avaliação dos resultados foi feita pela curva Receiver Operating Characteristic juntamente com a taxa de Equal Error Rate, sendo que, o método proposto, obteve a taxa de 4,9% contra 7,2% do método de Bozorth e de 2,0% contra 2,7% do Bozorth nos banco de dados dos anos de 2000 e 2002 respectivamente. Nos testes de robustez, o algoritmo proposto conseguiu identificar uma impressão digital com uma parte da imagem de apenas 30% do tamanho original e por se utilizar uma base de dados indexada, o mesmo obteve vantagens de tempo na recuperação de pequenas quantidades de impressões digitais de uma mesma classe. / The problem of the traditional methods of people identification is that they are based on passwords which may to be forgotten, stolen, lost, copied, stored in an insecure way and be used by unauthorized person. Automatic biometric systems appeared to provide an alternative for the recognition of people in a more safe and efficienty way. One most biometrics techniques used is the fingerprint recognition. With the increasing use of fingerprints in biometric systems, large fingerprint databases emerged, and with them, the challenge to find the best and fastest way to recover informations. According to the challenges previously mentioned, this work presents two proposals: i) to develop a newmetric algorithm for the identification of fingerprints and ii) to use it to index a fingerprint database using a metric search tree. To prove the efficiency of the developed algorithm tests were performed on two fingerprint images databases from Fingerprint Verification Competition of years 2000 and 2002. The obtained results were compared to the results of the algorithm proposed by Bozorth and was evaluated by the Receiver Operating Characteristic curve and the Equal Error Rate, where the proposed method is of 4.9% against 7.2% of Bozorth and 2.0% of the algorithm proposed against 2.7% of the Bozorth in the databases of the yearsof 2000 and 2002. In the robustness tests, the proposed algorithm as able to identify a fingerprint with only 30% of the original size and when using an a indexed database, it obtained better performance in the recovery of small amounts of fingerprints of a single class.
3

MFIS: algoritmo de reconhecimento e indexação em base de dados de impressões digitais em espaço métrico / MFIS: algorithm for the recognition and indexing in database of fingerprints in metric spaces

Jardini, Evandro de Araújo 31 August 2007 (has links)
O problema dos métodos tradicionais de identificação de pessoas é que são baseados em senhas e assim podem ser esquecidas, roubadas, perdidas, copiadas, armazenadas de maneira insegura e até utilizadas por uma pessoa que não tenha autorização. Os sistemas biométricos automáticos surgiram para oferecer uma alternativa para o reconhecimento de pessoas com maior segurança e eficiência. Uma das técnicas biométricas mais utilizadas é o reconhecimento de impressões digitais. Com o aumento do uso de impressões digitais nestes sistemas, houve o surgimento de grandes bancos de dados de impressões digitais, tornado-se um desafio encontrar a melhor e mais rápida maneira de recuperar informações. De acordo com os desafios apresentados, este trabalho tem duas propostas: i) desenvolver um novo algoritmo métrico para identificação de impressões digitais e ii) usá-lo para indexar um banco de dados de impressões digitais através de uma árvore de busca métrica. Para comprovar a eficiência do algoritmo desenvolvido foram realizados testes sobre duas bases de imagens de impressões digitais, disponibilizadas no evento Fingerprint Verification Competition dos anos de 2000 e 2002. Os resultados obtidos foram comparados com os resultados do algoritmo proposto por Bozorth. A avaliação dos resultados foi feita pela curva Receiver Operating Characteristic juntamente com a taxa de Equal Error Rate, sendo que, o método proposto, obteve a taxa de 4,9% contra 7,2% do método de Bozorth e de 2,0% contra 2,7% do Bozorth nos banco de dados dos anos de 2000 e 2002 respectivamente. Nos testes de robustez, o algoritmo proposto conseguiu identificar uma impressão digital com uma parte da imagem de apenas 30% do tamanho original e por se utilizar uma base de dados indexada, o mesmo obteve vantagens de tempo na recuperação de pequenas quantidades de impressões digitais de uma mesma classe. / The problem of the traditional methods of people identification is that they are based on passwords which may to be forgotten, stolen, lost, copied, stored in an insecure way and be used by unauthorized person. Automatic biometric systems appeared to provide an alternative for the recognition of people in a more safe and efficienty way. One most biometrics techniques used is the fingerprint recognition. With the increasing use of fingerprints in biometric systems, large fingerprint databases emerged, and with them, the challenge to find the best and fastest way to recover informations. According to the challenges previously mentioned, this work presents two proposals: i) to develop a newmetric algorithm for the identification of fingerprints and ii) to use it to index a fingerprint database using a metric search tree. To prove the efficiency of the developed algorithm tests were performed on two fingerprint images databases from Fingerprint Verification Competition of years 2000 and 2002. The obtained results were compared to the results of the algorithm proposed by Bozorth and was evaluated by the Receiver Operating Characteristic curve and the Equal Error Rate, where the proposed method is of 4.9% against 7.2% of Bozorth and 2.0% of the algorithm proposed against 2.7% of the Bozorth in the databases of the yearsof 2000 and 2002. In the robustness tests, the proposed algorithm as able to identify a fingerprint with only 30% of the original size and when using an a indexed database, it obtained better performance in the recovery of small amounts of fingerprints of a single class.
4

Fingerprint Segmentation

Jomaa, Diala January 2009 (has links)
In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.
5

Methods for Locating Distinct Features in Fingerprint Images / Methods for Locating Distinct Features in Fingerprint Images

Nelson, Jonas January 2002 (has links)
<p>With the advance of the modern information society, the importance of reliable identity authentication has increased dramatically. Using biometrics as a means for verifying the identity of a person increases both the security and the convenience of the systems. By using yourself to verify your identity such risks as lost keys and misplaced passwords are removed and by virtue of this, convenience is also increased. The most mature and well-developed biometric technique is fingerprint recognition. Fingerprints are unique for each individual and they do not change over time, which is very desirable in this application. There are multitudes of approaches to fingerprint recognition, most of which work by identifying so called minutiae and match fingerprints based on these. </p><p>In this diploma work, two alternative methods for locating distinct features in fingerprint images have been evaluated. The Template Correlation Method is based on the correlation between the image and templates created to approximate the homogenous ridge/valley areas in the fingerprint. The high-dimension of the feature vectors from correlation is reduced through principal component analysis. By visualising the dimension reduced data by ordinary plotting and observing the result classification is performed by locating anomalies in feature space, where distinct features are located away from the non-distinct. </p><p>The Circular Sampling Method works by sampling in concentric circles around selected points in the image and evaluating the frequency content of the resulting functions. Each images used here contains 30400 pixels which leads to sampling in many points that are of no interest. By selecting the sampling points this number can be reduced. Two approaches to sampling points selection has been evaluated. The first restricts sampling to occur only along valley bottoms of the image, whereas the second uses orientation histograms to select regions where there is no single dominant direction as sampling positions. For each sampling position an intensity function is achieved by circular sampling and a frequency spectrum of this function is achieved through the Fast Fourier Transform. Applying criteria to the relationships of the frequency components classifies each sampling location as either distinct or non-distinct. </p><p>Using a cyclic approach to evaluate the methods and their potential makes selection at various stages possible. Only the Circular Sampling Method survived the first cycle, and therefore all tests from that point on are performed on thismethod alone. Two main errors arise from the tests, where the most prominent being the number of spurious points located by the method. The second, which is equally serious but not as common, is when the method misclassifies visually distinct features as non-distinct. Regardless of the problems, these tests indicate that the method holds potential but that it needs to be subject to further testing and optimisation. These tests should focus on the three main properties of the method: noise sensitivity, radial dependency and translation sensitivity.</p>
6

MERGING OF FINGERPRINT SCANS OBTAINED FROM MULTIPLE CAMERAS IN 3D FINGERPRINT SCANNER SYSTEM

Boyanapally, Deepthi 01 January 2008 (has links)
Fingerprints are the most accurate and widely used biometrics for human identification due to their uniqueness, rapid and easy means of acquisition. Contact based techniques of fingerprint acquisition like traditional ink and live scan methods are not user friendly, reduce capture area and cause deformation of fingerprint features. Also, improper skin conditions and worn friction ridges lead to poor quality fingerprints. A non-contact, high resolution, high speed scanning system has been developed to acquire a 3D scan of a finger using structured light illumination technique. The 3D scanner system consists of three cameras and a projector, with each camera producing a 3D scan of the finger. By merging the 3D scans obtained from the three cameras a nail to nail fingerprint scan is obtained. However, the scans from the cameras do not merge perfectly. The main objective of this thesis is to calibrate the system well such that 3D scans obtained from the three cameras merge or align automatically. This error in merging is reduced by compensating for radial distortion present in the projector of the scanner system. The error in merging after radial distortion correction is then measured using the projector coordinates of the scanner system.
7

Metodologia para extração de características invariantes à rotação em imagens de impressões digitais / Methodology for the extraction of features invariant to the rotation in fingerprint images

Cristina Mônica Dornelas Mazetti 29 September 2006 (has links)
O objetivo deste trabalho é apresentar algoritmos aplicados para extração de características invariantes à rotação em imagens de impressões digitais. No pré-processamento da imagem utiliza-se detecção de bordas pelo detector de Canny tendo como resultado uma imagem binarizada e afinada. Na extração das minúcias a metodologia adotada é o número de cruzamentos (CN), que extrai os aspectos locais, tais como, as minúcias fim de linha e bifurcações. A direção das cristas locais não é utilizada porque nas imagens rotacionadas a condição de permanência das propriedades biométricas não são satisfeitas. A comparação das impressões digitais utiliza os vetores gerados pela extração de minúcias considerando a posição (x,y) da minúcia armazenada em um vetor por tipo de minúcia (um vetor para crista final e outro vetor para crista bifurcada) e calculando a distância Euclidiana dessa posição (x,y) ao centro de massa da distribuição de minúcias para cada tipo de minúcia. Assim, as duas imagens são similares quando a distância Euclidiana entre os vetores de cada imagem e por tipo de minúcia forem mínimas. São discutidas as limitações de outros trabalhos existentes envolvendo rotação, translação e distorção da imagem de impressão digital, mostrando que os poucos trabalhos que tratam o problema possuem resultados não satisfatórios. Os maiores problemas ocorridos foram a extração de minúcias espúrias, mas foram resolvidos com os métodos sugeridos por Dixon (1979), tendo resultados satisfatórios em duas metodologias. No método média, a precisão para encontrar uma imagem foi de 100%, duas imagens 97,32%, três imagens 92,35%, quatro imagens 86,41% e cinco imagens 71,86%. E no método normal, a precisão para encontrar uma imagem foi de 100%, duas imagens 99,20%, três imagens 96,95%, quatro imagens 94,00% e cinco imagens 76,43%. / The objective of this research is to present algorithms that can be applied in fingerprints images in order to extract certain features, which are invariant to an likely rotation in the given image. In the preprocessing stage, the Canny border detector is used, resulting in a binary, fine tuned image. For the minutiae extraction, the crossing number method is used, which extracts local aspects such as minutiae endings and bifurcations. The direction of local ridges is ignored because, in rotated images, the permanence conditions of the biometric attributes are not fulfilled. The process of matching fingerprints uses two arrays (one for ridge endings and the other for bifurcations), which are generated by the extraction of the minutiae, considering the (x,y) coordinate of the given minutiae stored in the arrays, and calculating its Euclidian distance relating to the center of mass of the minutiae distribution, for each of its types (ending or bifurcation). Thus, both images are similar when the Euclidian distance between the arrays of each image, distinct by the type of each minutiae, is minimal. The limitations of other pieces of research works concerning fingerprint image rotation, translation and distortion are discussed, indicating that the only few ones that deal with these kinds of problems give unsatisfactory results.
8

Methods for Locating Distinct Features in Fingerprint Images / Methods for Locating Distinct Features in Fingerprint Images

Nelson, Jonas January 2002 (has links)
With the advance of the modern information society, the importance of reliable identity authentication has increased dramatically. Using biometrics as a means for verifying the identity of a person increases both the security and the convenience of the systems. By using yourself to verify your identity such risks as lost keys and misplaced passwords are removed and by virtue of this, convenience is also increased. The most mature and well-developed biometric technique is fingerprint recognition. Fingerprints are unique for each individual and they do not change over time, which is very desirable in this application. There are multitudes of approaches to fingerprint recognition, most of which work by identifying so called minutiae and match fingerprints based on these. In this diploma work, two alternative methods for locating distinct features in fingerprint images have been evaluated. The Template Correlation Method is based on the correlation between the image and templates created to approximate the homogenous ridge/valley areas in the fingerprint. The high-dimension of the feature vectors from correlation is reduced through principal component analysis. By visualising the dimension reduced data by ordinary plotting and observing the result classification is performed by locating anomalies in feature space, where distinct features are located away from the non-distinct. The Circular Sampling Method works by sampling in concentric circles around selected points in the image and evaluating the frequency content of the resulting functions. Each images used here contains 30400 pixels which leads to sampling in many points that are of no interest. By selecting the sampling points this number can be reduced. Two approaches to sampling points selection has been evaluated. The first restricts sampling to occur only along valley bottoms of the image, whereas the second uses orientation histograms to select regions where there is no single dominant direction as sampling positions. For each sampling position an intensity function is achieved by circular sampling and a frequency spectrum of this function is achieved through the Fast Fourier Transform. Applying criteria to the relationships of the frequency components classifies each sampling location as either distinct or non-distinct. Using a cyclic approach to evaluate the methods and their potential makes selection at various stages possible. Only the Circular Sampling Method survived the first cycle, and therefore all tests from that point on are performed on thismethod alone. Two main errors arise from the tests, where the most prominent being the number of spurious points located by the method. The second, which is equally serious but not as common, is when the method misclassifies visually distinct features as non-distinct. Regardless of the problems, these tests indicate that the method holds potential but that it needs to be subject to further testing and optimisation. These tests should focus on the three main properties of the method: noise sensitivity, radial dependency and translation sensitivity.
9

Person Identification by Fingerprints and Voice / Asmens identifikavimas pagal pirštų atspaudus ir balsą

Kisel, Andrej 30 December 2010 (has links)
This dissertation focuses on person identification problems and proposes solutions to overcome those problems. First part is about fingerprint features extraction algorithm performance evaluation. Modifications to a known synthesis algorithm are proposed to make it fast and suitable for performance evaluation. Matching of deformed fingerprints is discussed in the second part of the work. New fingerprint matching algorithm that uses local structures and does not perform fingerprint alignment is proposed to match deformed fingerprints. The use of group delay features of linear prediction model for speaker recognition is proposed in the third part of the work. New similarity metric that uses group delay features is described. It is demonstrated that automatic speaker recognition system with proposed features and similarity metric outperforms traditional speaker identification systems . Multibiometrics using fingerprints and voice is addressed in the last part of the dissertation. / Penkiose disertacijos darbo dalyse nagrinėjamos žmogaus identifikavimo pagal pirštų atspaudus ir balsą problemos ir siūlomi jų sprendimai. Pirštų atspaudų požymių išskyrimo algoritmų kokybės įvertinimo problemą siūloma spręsti panaudojant sintezuotus pirštų atspaudus. Darbe siūlomos žinomo pirštų atpaudų sintezės algoritmo modifikacijos, kurios leidžia sukurti piršto atspaudo vaizdą su iš anksto nustatytomis charakteristikomis ir požymiais bei pagreitina sintezės procesą. Pirštų atspaudų požymių palyginimo problemos yra aptartos ir naujas palyginimo algoritmas yra siūlomas deformuotų pirštų palyginimui. Algoritmo kokybė yra įvertinta ant viešai prieinamų ir vidinių duomenų bazių. Naujas asmens identifikavimo pagal balsą metodas remiantis tiesinės prognozės modelio grupinės delsos požymiais ir tų požymių palyginimo metrika kokybės prasme lenkia tradicinius asmens identifikavimo pagal balsą metodus. Pirštų ir balso įrašų nepriklausomumas yra irodytas ir asmens atpažinimas pagal balsą ir pirštų atspaudus kartu yra pasiūlytas siekiant išspręsti bendras biometrinių sistemų problemas.
10

Biometric methods and mobile access control

Fransson, Linda, Jeansson, Therese January 2004 (has links)
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.

Page generated in 0.0984 seconds