261 research outputs found
Pattern mining approaches used in sensor-based biometric recognition: a review
Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems
A multimodal retina-iris biometric system using the levenshtein distance for spatial feature comparison
The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade-off-based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina-iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
Human Verification using Multiple Fingerprint Texture Matchers
This paper presents a multimodal biometric verification system using multiple fingerprint matchers. Theproposed verification system is based on multiple fingerprint matchers using Spatial Grey LevelDependence Method and Filterbank-based technique. The method independently extract fingerprinttexture features to generate matching scores. These individual normalized scores are combined into afinal score by the sum rule and the final score is eventually used to effect verification of a person asgenuine or an imposter. The matching scores are used in two ways: in first case equal weights are assignedto each matching scores and in second case user specific weights are used. The proposed verificationsystem has been tested on fingerprint database of FVC2002. The experimental results demonstrate that theproposed fusion strategy improves the overall accuracy of the system by reducing the total error rate of thesystem.Keywords: - Multimodal biometric System, Fingerprint verification, SGLDM, Filterbank matching, Scorelevel fusion, Sum rule
Robust multi-modal and multi-unit feature level fusion of face and iris biometrics
Multi-biometrics has recently emerged as a mean of more robust and effcient
personal verification and identification. Exploiting information from multiple
sources at various levels i.e., feature, score, rank or decision, the false acceptance
and rejection rates can be considerably reduced. Among all, feature level fusion
is relatively an understudied problem. This paper addresses the feature level
fusion for multi-modal and multi-unit sources of information. For multi-modal
fusion the face and iris biometric traits are considered, while the multi-unit fusion
is applied to merge the data from the left and right iris images. The proposed
approach computes the SIFT features from both biometric sources, either multi-
modal or multi-unit. For each source, the extracted SIFT features are selected via
spatial sampling. Then these selected features are finally concatenated together
into a single feature super-vector using serial fusion. This concatenated feature
vector is used to perform classification.
Experimental results from face and iris standard biometric databases are
presented. The reported results clearly show the performance improvements in
classification obtained by applying feature level fusion for both multi-modal and
multi-unit biometrics in comparison to uni-modal classification and score level
fusion
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
Recommended from our members
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
A Multimodal Biometric Authentication for Smartphones
Title from PDF of title page, viewed on October 18, 2016Dissertation advisor: Reza DerakhshaniVitaIncludes bibliographical references (pages 119-127)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015Biometrics is seen as a viable solution to ageing password based authentication on smartphones. Fingerprint biometric is leading the biometric technology for smartphones, however, owing to its high cost, major players in mobile industry are introducing fingerprint sensors only on their flagship devices, leaving most of their other devices without a fingerprint sensor. Cameras on the other hand have been seeing a constant upgrade in sensor and supporting hardware, courtesy of ‘selfies’ on all smartphones. Face, iris and visible vasculature are three biometric traits that can be captured in visible spectrum using existing cameras on smartphone. Current biometric recognition systems on smartphones rely on a single biometric trait for faster authentication thereby increasing the probability of failure to enroll, affecting the usability of the biometric system for practical purposes. While multibiometric system mitigates this problem, computational models for multimodal biometrics recognition on smartphones have scarcely been studied. This dissertation provides a practical multimodal biometric solution for existing smartphones using iris, periocular and eye vasculature biometrics. In this work, computational methods for quality analysis and feature detection of biometric data that are suitable for deployment on smartphones have been introduced. A fast, efficient feature detection algorithm (Vascular Point Detector) for identifying interest points on images garnered from both rear and front facing camera has been developed. It was observed that the retention ratio of VPD for final similarity score calculation was at least 10% higher than state of art interest point detectors such as FAST, over various datasets. An interest point suppression algorithm based on local histograms was introduced, reducing the computational footprint of matching algorithm by at least 30%. Further, experiments are presented which successfully combine multiple samples of eye vasculature, iris and periocular biometrics obtained from a single smartphone camera sensor. Several methods are explored to test the effectiveness of multi-modal and multi algorithm fusion at various levels of biometric recognition process, with the best algorithms performing under 2 second on an IPhone 5s. It is noted that the multimodal biometric system outperforms the unimodal biometric systems in terms of both performance and failure to enroll rates.Introduction -- Biometric systems -- Database -- Eye vaculature recognition -- Iris recognition in visible wavelength on smartphones -- Periocular recognition on smartphones -- Conclusions and future wor
On the Performance Improvement of Iris Biometric System
Iris is an established biometric modality with many practical applications. Its performance is influenced by noise, database size, and feature representation. This
thesis focusses on mitigating these challenges by efficiently characterising iris texture,developing multi-unit iris recognition, reducing the search space of large iris databases, and investigating if iris pattern change over time.To suitably characterise texture features of iris, Scale Invariant Feature Transform (SIFT) is combined with Fourier transform to develop a keypoint descriptor-F-SIFT. Proposed F-SIFT is invariant to transformation, illumination, and occlusion along with strong texture description property. For pairing the keypoints from gallery and probe iris images, Phase-Only Correlation (POC) function is used. The use of phase
information reduces the wrong matches generated using SIFT. Results demonstrate the effectiveness of F-SIFT over existing keypoint descriptors.To perform the multi-unit iris fusion, a novel classifier is proposed known
as Incremental Granular Relevance Vector Machine (iGRVM) that incorporates incremental and granular learning into RVM. The proposed classifier by design is
scalable and unbiased which is particularly suitable for biometrics. The match scores from individual units of iris are passed as an input to the corresponding iGRVM
classifier, and the posterior probabilities are combined using weighted sum rule. Experimentally, it is shown that the performance of multi-unit iris recognition improves
over single unit iris. For search space reduction, local feature based indexing approaches are developed
using multi-dimensional trees. Such features extracted from annular iris images are used to index the database using k-d tree. To handle the scalability issue of k-d tree,
k-d-b tree based indexing approach is proposed. Another indexing approach using R-tree is developed to minimise the indexing errors. For retrieval, hybrid coarse-to-fine
search strategy is proposed. It is inferred from the results that unification of hybrid search with R-tree significantly improves the identification performance.
Iris is assumed to be stable over time. Recently, researchers have reported that false rejections increase over the period of time which in turn degrades the performance. An empirical investigation has been made on standard iris aging databases to find whether iris patterns change over time. From the results, it is found that the rejections are primarily due to the presence of other covariates such as blur, noise, occlusion, pupil
dilation, and not due to agin
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