1,159 research outputs found
Mixing Biometric Data For Generating Joint Identities and Preserving Privacy
Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems
Biometrics
Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about
Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour
Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system’s performance is 90 % in signature verification system
Proof-of-Concept
Biometry is an area in great expansion and is considered as possible solution to cases where high
authentication parameters are required. Although this area is quite advanced in theoretical
terms, using it in practical terms still carries some problems. The systems available still depend
on a high cooperation level to achieve acceptable performance levels, which was the backdrop
to the development of the following project. By studying the state of the art, we propose the
creation of a new and less cooperative biometric system that reaches acceptable performance
levels.A constante necessidade de parâmetros mais elevados de segurança, nomeadamente ao nível
de autenticação, leva ao estudo biometria como possível solução. Actualmente os mecanismos
existentes nesta área tem por base o conhecimento de algo que se sabe ”password” ou algo
que se possui ”codigo Pin”. Contudo este tipo de informação é facilmente corrompida ou contornada.
Desta forma a biometria é vista como uma solução mais robusta, pois garante que a
autenticação seja feita com base em medidas físicas ou compartimentais que definem algo que
a pessoa é ou faz (”who you are” ou ”what you do”).
Sendo a biometria uma solução bastante promissora na autenticação de indivíduos, é cada vez
mais comum o aparecimento de novos sistemas biométricos. Estes sistemas recorrem a medidas
físicas ou comportamentais, de forma a possibilitar uma autenticação (reconhecimento) com
um grau de certeza bastante considerável. O reconhecimento com base no movimento do corpo
humano (gait), feições da face ou padrões estruturais da íris, são alguns exemplos de fontes
de informação em que os sistemas actuais se podem basear. Contudo, e apesar de provarem
um bom desempenho no papel de agentes de reconhecimento autónomo, ainda estão muito
dependentes a nível de cooperação exigida. Tendo isto em conta, e tudo o que já existe no
ramo do reconhecimento biometrico, esta área está a dar passos no sentido de tornar os seus
métodos o menos cooperativos poss??veis. Possibilitando deste modo alargar os seus objectivos
para além da mera autenticação em ambientes controlados, para casos de vigilância e controlo
em ambientes não cooperativos (e.g. motins, assaltos, aeroportos).
É nesta perspectiva que o seguinte projecto surge. Através do estudo do estado da arte, pretende
provar que é possível criar um sistema capaz de agir perante ambientes menos cooperativos,
sendo capaz de detectar e reconhecer uma pessoa que se apresente ao seu alcance.O
sistema proposto PAIRS (Periocular and Iris Recognition Systema) tal como nome indica, efectua
o reconhecimento através de informação extraída da íris e da região periocular (região circundante
aos olhos). O sistema é construído com base em quatro etapas: captura de dados,
pré-processamento, extração de características e reconhecimento. Na etapa de captura de
dados, foi montado um dispositivo de aquisição de imagens com alta resolução com a capacidade
de capturar no espectro NIR (Near-Infra-Red). A captura de imagens neste espectro tem
como principal linha de conta, o favorecimento do reconhecimento através da íris, visto que
a captura de imagens sobre o espectro visível seria mais sensível a variações da luz ambiente.
Posteriormente a etapa de pré-processamento implementada, incorpora todos os módulos do
sistema responsáveis pela detecção do utilizador, avaliação de qualidade de imagem e segmentação
da íris. O modulo de detecção é responsável pelo desencadear de todo o processo, uma
vez que esta é responsável pela verificação da exist?ncia de um pessoa em cena. Verificada
a sua exist?ncia, são localizadas as regiões de interesse correspondentes ? íris e ao periocular,
sendo também verificada a qualidade com que estas foram adquiridas. Concluídas estas
etapas, a íris do olho esquerdo é segmentada e normalizada. Posteriormente e com base em
vários descritores, é extraída a informação biométrica das regiões de interesse encontradas,
e é criado um vector de características biométricas. Por fim, é efectuada a comparação dos
dados biometricos recolhidos, com os já armazenados na base de dados, possibilitando a criação
de uma lista com os níveis de semelhança em termos biometricos, obtendo assim um resposta
final do sistema. Concluída a implementação do sistema, foi adquirido um conjunto de imagens capturadas através do sistema implementado, com a participação de um grupo de voluntários.
Este conjunto de imagens permitiu efectuar alguns testes de desempenho, verificar e afinar
alguns parâmetros, e proceder a optimização das componentes de extração de características e
reconhecimento do sistema. Analisados os resultados foi possível provar que o sistema proposto
tem a capacidade de exercer as suas funções perante condições menos cooperativas
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A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.
Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level.
Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image.
Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level.
Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image.
Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira
Multimodal Three Dimensional Scene Reconstruction, The Gaussian Fields Framework
The focus of this research is on building 3D representations of real world scenes and objects using different imaging sensors. Primarily range acquisition devices (such as laser scanners and stereo systems) that allow the recovery of 3D geometry, and multi-spectral image sequences including visual and thermal IR images that provide additional scene characteristics. The crucial technical challenge that we addressed is the automatic point-sets registration task. In this context our main contribution is the development of an optimization-based method at the core of which lies a unified criterion that solves simultaneously for the dense point correspondence and transformation recovery problems. The new criterion has a straightforward expression in terms of the datasets and the alignment parameters and was used primarily for 3D rigid registration of point-sets. However it proved also useful for feature-based multimodal image alignment. We derived our method from simple Boolean matching principles by approximation and relaxation. One of the main advantages of the proposed approach, as compared to the widely used class of Iterative Closest Point (ICP) algorithms, is convexity in the neighborhood of the registration parameters and continuous differentiability, allowing for the use of standard gradient-based optimization techniques. Physically the criterion is interpreted in terms of a Gaussian Force Field exerted by one point-set on the other. Such formulation proved useful for controlling and increasing the region of convergence, and hence allowing for more autonomy in correspondence tasks. Furthermore, the criterion can be computed with linear complexity using recently developed Fast Gauss Transform numerical techniques. In addition, we also introduced a new local feature descriptor that was derived from visual saliency principles and which enhanced significantly the performance of the registration algorithm. The resulting technique was subjected to a thorough experimental analysis that highlighted its strength and showed its limitations. Our current applications are in the field of 3D modeling for inspection, surveillance, and biometrics. However, since this matching framework can be applied to any type of data, that can be represented as N-dimensional point-sets, the scope of the method is shown to reach many more pattern analysis applications
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