1,489 research outputs found

    OSPCV: Off-line Signature Verification using Principal Component Variances

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    Signature verification system is always the most sought after biometric verification system. Being a behavioral biometric feature which can always be imitated, the researcher faces a challenge in designing such a system, which has to counter intrapersonal and interpersonal variations. The paper presents a comprehensive way of off-line signature verification based on two features namely, the pixel density and the centre of gravity distance. The data processing consists of two parallel processes namely Signature training and Test signature analysis. Signature training involves extraction of features from the samples of database and Test signature analysis involves extraction of features from test signature and it’s comparison with those of trained values from database. The features are analyzed using Principal Component Analysis (PCA). The proposed work provides a feasible result and a notable improvement over the existing systems

    Kepler's First Rocky Planet: Kepler-10b

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    NASA's Kepler Mission uses transit photometry to determine the frequency of Earth-size planets in or near the habitable zone of Sun-like stars. The mission reached a milestone toward meeting that goal: the discovery of its first rocky planet, Kepler-10b. Two distinct sets of transit events were detected: (1) a 152 ± 4 ppm dimming lasting 1.811 ± 0.024 hr with ephemeris T [BJD] = 2454964.57375^(+0.00060)_(–0.00082) + N * 0.837495^(+0.000004)_(–0.000005) days and (2) a 376 ± 9 ppm dimming lasting 6.86 ± 0.07 hr with ephemeris T [BJD] = 2454971.6761^(+0.0020)_(–0.0023) + N * 45.29485^(+0.00065) _(–0.00076) days. Statistical tests on the photometric and pixel flux time series established the viability of the planet candidates triggering ground-based follow-up observations. Forty precision Doppler measurements were used to confirm that the short-period transit event is due to a planetary companion. The parent star is bright enough for asteroseismic analysis. Photometry was collected at 1 minute cadence for >4 months from which we detected 19 distinct pulsation frequencies. Modeling the frequencies resulted in precise knowledge of the fundamental stellar properties. Kepler-10 is a relatively old (11.9 ± 4.5 Gyr) but otherwise Sun-like main-sequence star with T_(eff) = 5627 ± 44 K, M_⋆ = 0.895 ± 0.060 M_⊙ , and R_⋆ = 1.056 ± 0.021 R_⊙. Physical models simultaneously fit to the transit light curves and the precision Doppler measurements yielded tight constraints on the properties of Kepler-10b that speak to its rocky composition: M_P = 4.56^9+1.17)_(–1.29) M_⊕, R_P = 1.416^(+0.033)_(–0.036) R_⊕, and ρ_P = 8.8^(+2.1)_(–2.9) g cm^(–3). Kepler-10b is the smallest transiting exoplanet discovered to date

    Intra-modal Score level Fusion for Off-line Signature Verification

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    Signature is widely used as a means of personal verification which emphasizes the need for a signature verification system. Often the single signature feature may produce unacceptable error rates. In this paper, Intra-modal Score level Fusion for Off-line Signature Verification (ISFOSV) is proposed. The scanned signature image is skeletonized and exact signature area is obtained by preprocessing. In the first stage 60 centers of signature are extracted by horizontal and vertical splitting. In the second stage the 168 features are extracted in two phases. The phase one consists of dividing the signature into 128 blocks using the center of signature by counting the number of black pixels and the angular feature in each block is determined to generate 128 angular features. In the second phase the signature is divided into 40 blocks from each of the four corners of the signature to generate 40 angular features. Totally 168 angular features are extracted from phase one and two to verify the signature. The centers of signature are compared using correlation and the distance between the angular features of the genuine and test signatures is computed. The correlation matching score and distance matching score of the signature are fused to verify the authenticity. A mathematical model is proposed to further optimize the results. It is observed that the proposed model has better FAR, FRR and EER values compared to the existing algorithms

    QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios

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    The concerns about individuals security have justified the increasing number of surveillance cameras deployed both in private and public spaces. However, contrary to popular belief, these devices are in most cases used solely for recording, instead of feeding intelligent analysis processes capable of extracting information about the observed individuals. Thus, even though video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant details about the subjects that took part in a crime depends on the manual inspection of recordings. As such, the current goal of the research community is the development of automated surveillance systems capable of monitoring and identifying subjects in surveillance scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at designing a visual surveillance system capable of acquiring biometric data at a distance (e.g., face, iris or gait) without requiring human intervention in the process, as well as devising biometric recognition methods robust to the degradation factors resulting from the unconstrained acquisition process. Regarding the first goal, the analysis of the data acquired by typical surveillance systems shows that large acquisition distances significantly decrease the resolution of biometric samples, and thus their discriminability is not sufficient for recognition purposes. In the literature, diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring high-resolution imagery at a distance, particularly when using a master-slave configuration. In the master-slave configuration, the video acquired by a typical surveillance camera is analyzed for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged at high-resolution by the PTZ camera. Several methods have already shown that this configuration can be used for acquiring biometric data at a distance. Nevertheless, these methods failed at providing effective solutions to the typical challenges of this strategy, restraining its use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development of a biometric data acquisition system based on the cooperation of a PTZ camera with a typical surveillance camera. The first proposal is a camera calibration method capable of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ camera. The second proposal is a camera scheduling method for determining - in real-time - the sequence of acquisitions that maximizes the number of different targets obtained, while minimizing the cumulative transition time. In order to achieve the first goal of this thesis, both methods were combined with state-of-the-art approaches of the human monitoring field to develop a fully automated surveillance capable of acquiring biometric data at a distance and without human cooperation, designated as QUIS-CAMPI system. The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis of the performance of the state-of-the-art biometric recognition approaches shows that these approaches attain almost ideal recognition rates in unconstrained data. However, this performance is incongruous with the recognition rates observed in surveillance scenarios. Taking into account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a distance ranging from 5 to 40 meters and without human intervention in the acquisition process. This set allows to objectively assess the performance of state-of-the-art biometric recognition methods in data that truly encompass the covariates of surveillance scenarios. As such, this set was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained by the nine methods specially designed for this competition. In addition, the data acquired by the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the development of methods robust to the covariates of surveillance scenarios. The first proposal regards a method for detecting corrupted features in biometric signatures inferred by a redundancy analysis algorithm. The second proposal is a caricature-based face recognition approach capable of enhancing the recognition performance by automatically generating a caricature from a 2D photo. The experimental evaluation of these methods shows that both approaches contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivíduos tem justificado o crescimento do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos. Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de monitorizar e identificar indivíduos em ambientes de vídeo-vigilância. Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se 1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias (e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas. No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis. Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave. Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse (e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo, esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações que maximiza o número de diferentes sujeitos observados e simultaneamente minimiza o tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os dois métodos propostos foram combinados com os avanços alcançados na área da monitorização de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação dos indivíduos no processo, designado por sistema QUIS-CAMPI. O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais, esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos capturados num ambiente real de vídeo-vigilância. Como tal, este conjunto foi utilizado para promover a primeira competição de reconhecimento biométrico em ambientes não controlados. Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9 métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para aumentar a robustez aos fatores de degradação observados em ambientes de vídeo-vigilância. O primeiro é um método para detetar características corruptas em assinaturas biométricas através da análise da redundância entre subconjuntos de características. O segundo é um método de reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir as taxas de erro em dados adquiridos de forma não controlada

    The Design of an Imager to Safeguard Spent Fuel Using Passive Fast Neutron Emission Tomography

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    Safeguarding spent fuel in spent fuel pools, during transportation, and at dry cask storage sites has been a continuing priority for the International Atomic Energy Agency (IAEA.) The IAEA implements partial defect testing on all easily dismountable fuel before transfer to difficult-to-access storage. This project is focused on developing a new imaging capability using fast neutron emission tomography in support of the IAEA’s mission. The capability is intended to address the buildup of spent fuel inventories around the world from decommissioning activities by creating an efficient and effective tool for verification of a variety of fuel types for long-term disposition.While the sensitivity of gamma emission tomography is limited by self-attenuation, neutron measurements may have better sensitivity for resolving individual pins toward the center of larger fuel assemblies. Because the neutron signal originates primarily from 244Cm, which is sensitive to exposure, this method could also be sensitive to assemblies containing fuel pins replaced after a single cycle in the reactor and subsequently irradiated in the core. This work describes a set of simulation and measurement work completed in order to investigate and converge on the final design of a fast neutron emission tomography system for imaging a spent nuclear fuel assembly. To conduct a constrained optimization for the design, a range of imager design parameters were identified to be varied, and MCNP was used to build hundreds of geometries to investigate. The analysis was split in two components for gamma or neutron analysis. Simulations and proof-of-concept measurements presented here suggest that it is viable to build a compact equivalent to a parallel slit collimator imager that has sufficient spatial resolution to image spent fuel pins. Furthermore, it is expected to be able to withstand the high photon rates present in the relevant environment. Recommended future work is also discussed

    Guidance for benthic habitat mapping: an aerial photographic approach

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    This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

    Biometric Identification Systems: Feature Level Clustering of Large Biometric Data and DWT Based Hash Coded Bar Biometric System

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    Biometric authentication systems are fast replacing conventional identification schemes such as passwords and PIN numbers. This paper introduces a novel matching scheme that uses a image hash scheme. It uses Discrete Wavelet Transformation (DWT) of biometric images and randomized processing strategies for hashing. In this scheme the input image is decomposed into approximation, vertical, horizontal and diagonal coefficients using the discrete wavelet transform. The algorithm converts images into binary strings and is robust against compression, distortion and other transformations. As a case study the system is tested on ear database and is outperforming with an accuracy of 96.37% with considerably low FAR of 0.17%. The performance shows that the system can be deployed for high level security applications
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