Thematische Bibliographien / ECG biometry

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Auswahl der wissenschaftlichen Literatur zum Thema „ECG biometry“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 14. Februar 2022

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Zeitschriftenartikel zum Thema "ECG biometry"

Luz, Eduardo José da S., David Menotti und William Robson Schwartz. „Evaluating the use of ECG signal in low frequencies as a biometry“. Expert Systems with Applications 41, Nr. 5 (April 2014): 2309–15. http://dx.doi.org/10.1016/j.eswa.2013.09.028.

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Kumar, Sunil. „ECG Biometric Identification“. International Journal for Research in Applied Science and Engineering Technology 6, Nr. 3 (31.03.2018): 2148–52. http://dx.doi.org/10.22214/ijraset.2018.3505.

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Yoon, Seok-Joo, und Gwang-Jun Kim. „Personal Biometric Identification based on ECG Features“. Journal of the Korea institute of electronic communication sciences 10, Nr. 4 (30.04.2015): 521–26. http://dx.doi.org/10.13067/jkiecs.2015.10.4.521.

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Karimian, Nima, Damon Woodard und Domenic Forte. „ECG Biometric: Spoofing and Countermeasures“. IEEE Transactions on Biometrics, Behavior, and Identity Science 2, Nr. 3 (Juli 2020): 257–70. http://dx.doi.org/10.1109/tbiom.2020.2992274.

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Donida Labati, Ruggero, Enrique Muñoz, Vincenzo Piuri, Roberto Sassi und Fabio Scotti. „Deep-ECG: Convolutional Neural Networks for ECG biometric recognition“. Pattern Recognition Letters 126 (September 2019): 78–85. http://dx.doi.org/10.1016/j.patrec.2018.03.028.

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Yadav, Nisha, Manoj Duhan und Anil Rose. „Biometric Human Recognition using ECG Signals“. IARJSET 4, Nr. 6 (20.06.2017): 168–71. http://dx.doi.org/10.17148/iarjset.2017.4630.

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Odinaka, Ikenna, Po-Hsiang Lai, Alan D. Kaplan, Joseph A. O'Sullivan, Erik J. Sirevaag und John W. Rohrbaugh. „ECG Biometric Recognition: A Comparative Analysis“. IEEE Transactions on Information Forensics and Security 7, Nr. 6 (Dezember 2012): 1812–24. http://dx.doi.org/10.1109/tifs.2012.2215324.

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Ingale, Mohit, Renato Cordeiro, Siddartha Thentu, Younghee Park und Nima Karimian. „ECG Biometric Authentication: A Comparative Analysis“. IEEE Access 8 (2020): 117853–66. http://dx.doi.org/10.1109/access.2020.3004464.

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Kadhim Abed, Jameel, und Nahrain N. Abd. „ECG Biometric Verification by using PCA“. International Journal of Engineering Trends and Technology 65, Nr. 1 (25.11.2018): 4–8. http://dx.doi.org/10.14445/22315381/ijett-v65p202.

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BE, Manjunathswamy, Appaji M Abhishek, Thriveni J, Venugopal K R und L. M Patnaik. „Multimodal Biometric Authentication using ECG and Fingerprint“. International Journal of Computer Applications 111, Nr. 13 (18.02.2015): 33–39. http://dx.doi.org/10.5120/19601-1452.

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Dissertationen zum Thema "ECG biometry"

Waloszek, Vojtěch. „Identifikace a verifikace osob pomocí záznamu EKG“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442492.

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In the past years, utilization of ECG for verification and identification in biometry is investigated. The topic is investigated in this thesis. Recordings from ECG ID database from PhysioNet and our own ECG recordings recorded using Apple Watch 4 are used for training and testing this method. Many of the existing methods have proven the possibility of using ECG for biometry, however they were using clinical ECG devices. This thesis investigates using recordings from wearable devices, specifically smart watch. 16 features are extracted from ECG recordings and a random forest classifier is used for verification and identification. The features include time intervals between fiducial points, voltage difference between fiducial points and PR intervals variability in a recording. The average performance of verification model of 14 people is TRR 96,19 %, TAR 84,25 %.

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Farina, André Marcelo. „Biomobile : sistema de identificação de usuários em dispositivos móveis na plataforma Android utilizando reconhecimento de faces a partir de vídeo /“. São José do Rio Preto : [s.n.], 2012. http://hdl.handle.net/11449/89344.

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Orientador: Aparecido Nilceu Marana
Banca: João Eduardo Machado Perea
Banca: Fátima de Lourdes dos Santos Nunes Marques
Resumo: Na era da informação em que vivemos a demanda por conectividade e acesso a dados é crescente. Neste cenário os dispositivos móveis estão se tornando cada vez mais populares. Com o aumento da demanda por este tipo de dispositivo aumenta também o volume de informações neles armazenadas. Com isso, os dispositivos móveis estão se tornando uma importante fonte de informação que precisam de mecanismos adequados de proteção. O objetivo desta dissertação de mestrado foi promover um estudo sobre a viabilidade da utilização de Biometria para a proteção de informações dos usuários em dispositivos móveis, em particular nos smartphones, via reconhecimento de faces. A opção pelo reconhecimento facial deveu-se ao fato dos smartphones proverem naturalmente recursos de hardware para a aquisição de vídeos. Para a realização do estudo proposto, foi desenvolvido um sistema de reconhecimento facial na plataforma Android, denominado BioMobile, cuja arquitetura foi projetada para permitir a execução integralmente no domínio dos dispositivos móveis. Para a detecção das faces, foi adotado o algoritmo Viola-Jones, enquanto que para o reconhecimento foram usados os descritores PCA e LBP. O BioMobile foi desenvolvido para operar nos modos de autenticação e de reconhecimento. Além disso, ele trabalha com vídeos, o que possibilita a adoção da técnica de maioria de votos, tornando-o mais tolerante a falhas e mais preciso. Os resultados experimentais mostraram que, ao contrário do algoritmo baseado em LBP, o algoritmo baseado em PCA se torna inviável quando usado em dispositivos com restrições de memória e de processamento. Os resultados obtidos também indicam que o modo de operação de reconhecimento quando aplicado totalmente no domínio dos dispositivos móveis não é conveniente em termos... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: In the information age in which we live the demand for connectivity and data access is growing. In this scenario the mobile devices are becoming increasingly popular, especially the smart phones. The increasing demand for this type of device leads to high personal and professional information storage. Therefore, mobile devices are becoming important sources of information that need proper protection mechanisms. The purpose of this master dissertation was to carry out a study on the feasibility of using biometrics as a means of safe, efficient and appropriate information protection on mobile devices, especially in smart phones, via user identification through face recognition. The choice for facial recognition was due to the fact that the smart phones provide embedded video cameras. In order to conduct the proposed study, it was developed a face recognition system on the Android platform, called BioMobile, whose architecture was designed to allow its full execution without the need of external servers. For face detection it was adopted the Viola-Jones algorithm, while for face recognition it were assessed the PCA and LBP descriptors. BioMobile was also designed to operate in authentication and recognition modes. Besides, it works with videos instead of still images that allow the adoption of the technique of majority voting becoming it more fault-tolerant and precise. The experimental results showed that the PCA algorithm is not feasible when used in devices with memory and processing power limitations. LBP, in contrast, showed to be appropriated. The results also indicated that the recognition operation mode fully executed in the mobile devices is not so convenient due to the processing time required. On the other hand, the authentication operation mode showed good performance regarding this issue. The system also showed few error rates (around 2%) depending on the adopted configuration
Mestre

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Arteaga, Falconi Juan Sebastian. „Towards an Accurate ECG Biometric Authentication System with Low Acquisition Time“. Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40129.

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Biometrics is the study of physical or behavioral traits that establishes the identity of a person. Forensics, physical security and cyber security are some of the main fields that use biometrics. Unlike traditional authentication systems—such as password based—biometrics cannot be lost, forgotten or shared. This is possible because biometrics establishes the identity of a person based on a physiological/behavioural characteristic rather than what the person possess or remembers. Biometrics has two modes of operation: identification and authentication. Identification finds the identity of a person among a group of persons. Authentication determines if the claimed identity of a person is truthful. Biometric person authentication is an alternative to passwords or graphical patterns. It prevents shoulder surfing attacks, i.e., people watching from a short distance. Nevertheless, biometric traits of conventional authentication techniques like fingerprints, face—and to some extend iris—are easy to capture and duplicate. This denotes a security risk for modern and future applications such as digital twins, where an attacker can copy and duplicate a biometric trait in order to spoof a biometric system. Researchers have proposed ECG as biometric authentication to solve this problem. ECG authentication conceals the biometric traits and reduces the risk of an attack by duplication of the biometric trait. However, current ECG authentication solutions require 10 or more seconds of an ECG signal in order to have accurate results. The accuracy is directly proportional to the ECG signal time-length for authentication. This is inconvenient to implement ECG authentication in an end-user product because a user cannot wait 10 or more seconds to gain access in a secure manner to their device. This thesis addresses the problem of spoofing by proposing an accurate and secure ECG biometric authentication system with relatively short ECG signal length for authentication. The system consists of an ECG acquisition from lead I (two electrodes), signal processing approaches for filtration and R-peak detection, a feature extractor and an authentication process. To evaluate this system, we developed a method to calculate the Equal Error Rate—EER—with non-normal distributed data. In the authentication process, we propose an approach based on Support Vector Machine—SVM—and achieve 4.5% EER with 4 seconds of ECG signal length for authentication. This approach opens the door for a deeper understanding of the signal and hence we enhanced it by applying a hybrid approach of Convolutional Neural Networks—CNN—combined with SVM. The purpose of this hybrid approach is to improve accuracy by automatically detect and extract features with Deep Learning—in this case CNN—and then take the output into a one-class SVM classifier—Authentication; which proved to outperform accuracy for one-class ECG classification. This hybrid approach reduces the EER to 2.84% with 4 seconds of ECG signal length for authentication. Furthermore, we investigated the combination of two different biometrics techniques and we improved the accuracy to 0.46% EER, while maintaining a short ECG signal length for authentication of 4 seconds. We fuse Fingerprint with ECG at the decision level. Decision level fusion requires information that is available from any biometric technique. Fusion at different levels—such as feature level fusion—requires information about features that are incompatible or hidden. Fingerprint minutiae are composed of information that differs from ECG peaks and valleys. Therefore fusion at the feature level is not possible unless the fusion algorithm provides a compatible conversion scheme. Proprietary biometric hardware does not provide information about the features or the algorithms; therefore, features are hidden and not accessible for feature level fusion; however, the result is always available for a decision level fusion.

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Bin, Safie Sairul Izwan. „Pulse domain novel feature extraction methods with application to ecg biometric authentication“. Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17829.

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This thesis presents the concept of representing finite signals in terms of sequential output pulses called pulse domain to extract Electrocardiogram (ECG) features for biometric authentication systems. Two novel methods based on the pulse domain philosophy namely Pulse Active (PA) and Adaptive Pulse Active (APA) techniques are presented in this thesis. A total of 11 algorithms are derived from these two methods and used to generate novel ECG feature vectors. Six algorithms of the PA technique are named as Pulse Active Bit (PAB), Pulse Active Width (PAW), Pulse Active Area (PAA), Pulse Active Mean (PAM), Pulse Active Ratio (PAR) and Pulse Active Harmonic (PAH). Five APA algorithms are named as Adaptive Pulse Active Bit (APAB), Adaptive Pulse Active Width (APAW), Adaptive Pulse Active Area (APAA), Adaptive Pulse Active Mean (APAM) and Adaptive Pulse Active Harmonic (APAH). The proposed techniques are validated using ECG experimental data from 112 subjects. Simulation results indicate that APAW generates the best biometric performance of all 11 algorithms. Selected ranges of PA and APA parameters are determined in this thesis that generates approximate similar biometric performance. Using this suggested range, these parameters are than used as a personal identification number (PIN) which are a part of the proposed PA-APA ECG based multilevel security biometric authentication system.

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Neves, Camila de Castro. „Morfologia e biometria das glândulas vesiculares e bulbouretrais de ovinos castrados e não castrados da raça Santa Inês /“. Jaboticabal : [s.n.], 2010. http://hdl.handle.net/11449/101121.

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Orientadora: Silvana Martinez Baraldi Artoni
Banca: Carlos Alberto Vicentini
Banca: Marcos Lania de Araújo
Banca: Maria Rita Pacheco
Banca: Vanessa Sobue Franzo
Resumo: Neste trabalho foram utilizados 14 ovinos machos adultos, seis castrados e oito não castrados da raça Santa Inês com o peso médio de 32 kg. Foi realizado estudo macroscópico (topográfico e biométrico) e microscópico (histológico) das glândulas vesiculares e bulbouretrais com o objetivo de averiguar o comportamento dessas glândulas nos animais castrados e não castrados. Para o estudo macroscópico realizou-se a descrição destas duas glândulas, e avaliou-se a biometria após a dissecação e mensuração do peso, comprimento, altura e a largura das respectivas glândulas. Para a histologia foram obtidos fragmentos das respectivas glândulas os quais foram recortados, obteu-se cortes histológicos de cinco micrômetros sendo diafanizados em benzol e processados para inclusão do material em paraplast e corados com Hematoxilina-eosina e fotodocumentados em fotomicroscópio Olympus BX 50. Morfologicamente pode-se documentar que toda a sua topografia apresentou semelhança à espécie bovina, e na biometria evidenciouse medidas macroscópicas menores (p<0,05) das glândulas de ovinos castrados em relação aos não castrados, exceto para o peso vivo dos animais e peso relativo das glândulas vesiculares (p>0,05). Histologicamente verificou-se que a glândula vesicular é um tubo enrolado sobre si mesmo seccionado em diferentes posições. Separando-o há uma delgada camada de tecido conjuntivo frouxo. A mucosa é pregueada e o epitélio é prismático simples com células altas ricas. A lâmina própria possui fibras elásticas e está envolta por uma camada muscular lisa, constituída por duas lâminas: uma interna, de fibras circulares e outra externa, de fibras longitudinais. A camada adventícia é constituída por tecido conjuntivo frouxo. Os ductos excretores principais estão revestidos por epitélio prismático biestratificado... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: In this project, 14 adult male sheep were used. six of them were castrated and eight non-castrated, and they were all Santa Ines breed, weighing 32kg in average. A macroscopic (topographic and biometric) and microscopic (histologic) study of the vesicular and bulbourethral glands was carried out with the aim of investigating the behavior of these glands in the castrated and non-castrated animals. For the macroscopic study, the description of these two glands was carried out, and the biometry after the dissecation, and weight, length, height and width measurement of the respective glands was evaluated. For the histology, fragments of these glands were obtained and cut. The obtained histological cuts of five micrometers were diaphanized in benzol and processed for the inclusion of the material in paraplast and stained with Hematoxylin and eosin and photo documented in photomicroscope Olympus BX 50. Morphologically, all its topography can be documented presenting similarity to the bovine species, and in the biometry it has evidenced smaller macroscopic measurements (p<0,05) comparing the glands of the castrated ovines and the non-castrated ones, except for the live weight of the animals and relative weight of the vesicular glands (p>0,05). Histologically, it has been verified that the vesicular gland is a tube coiled round itself sectioned at different positions. There is a thin layer of loose connective tissue separating it. The mucosa is wrinkled and the simple prismatic epithelium is composed of high cells that are rich. The blade itself has elastic fibers and is wrapped by a layer of smooth muscle, composed by two blades: an internal one, composed of circular fibers and an external one, composed of longitudinal fibers. The tunica adventitia is composed by loose connective tissue. The main excretory ducts are covered by bistratified prismatic epithelium... (Complete abstract click electronic access below)
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Tasso, Júnior Luiz Carlos. „Caracterização agrotecnológica de cultivares de cana-de-açúcar (Saccharum spp.) na região centro-norte do estado de São Paulo /“. Jaboticabal : [s.n.], 2007. http://hdl.handle.net/11449/105231.

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Orientador: Marcos Omir Marques
Banca: Carlos Alberto Mathias Azania
Banca: Marcos Guimarães de Andrade Landell
Banca: Miguel Angelo Mutton
Banca: Ailto Antonio Casagrande
Resumo: Estudos de comparação entre cultivares de cana-de-açúcar são importantes especialmente em novas regiões produtoras. O presente trabalho foi conduzido em duas propriedades agrícolas da região centro norte do Estado de São Paulo. Foram cultivadas 20 variedades de cana-de-açúcar. O delineamento experimental foi em blocos ao acaso com 20 tratamentos e 4 repetições. As variedades estudadas foram: IAC91-2195, SP89-1115, RB855453, RB855156, RB835486 (precoces); SP87-365, SP81-3250, SP80-1842, SP80-1816, SP79-1011, RB92-8064, IAC91-5155, IAC87-3396, SP90-3414, SP90-1638 (médias), IACSP93-6006, IAC91- 3186, SP83-2847, RB867515 e RB72454 (tardias). Foram avaliadas variáveis fenotípicas, biométricas, produtividade, tecnológicas além do acúmulo de macro e micronutrientes em colmo e palhada. Concluiu-se que as variedades que apresentaram as melhores performances foram: RB855453, RB835456, RB928064, RB867515 e a IAC91-3186. O acúmulo de nutrientes (colmos, folhas verdes e ponteiros), foi influenciado pelas variedades e pelas condições edafoclimáticas.
Abstract: Studies about sugarcane cultivars comparison are very important in news sugarcane regions producers. This research was conducted out with the objective to study 20 sugarcane cultivars cropped in two agricultural areas at São Paulo State center-north region. The experimental design was a randomized blocks with 20 treatments and 4 replications. The following cultivars were studied: IAC91-2195, SP89- 1115, RB855453, RB855156, RB835486 (early maturation period); SP87-365, SP81- 3250, SP80-1842, SP80-1816, SP79-1011, RB92-8064, IAC91-5155, IAC87-3396, SP90-3414, SP90-1638 (middle maturation period), IACSP93-6006, IAC91-3186, SP83- 2847, RB867515 e RB72454 (late maturation period). It was concluded that the best behaviors were shown by RB855453, RB835456, RB928064, RB867515 and IAC91- 3186. Stalk, green leaf, dry leaf, heart nutrients accumulation and nutrients exportation changed according the sugarcane cultivars, edaphology and climatologic conditions.
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Yang, Su. „The use of EEG signals for biometric person recognition“. Thesis, University of Kent, 2015. https://kar.kent.ac.uk/53681/.

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This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases.

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Favoretto, Saulo. „Máquinas de aprendizado extremo aplicadas à identificação de pessoas através de eletrocardiograma (ECG)“. Universidade Tecnológica Federal do Paraná, 2016. http://repositorio.utfpr.edu.br/jspui/handle/1/2417.

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Capes
Esta pesquisa estuda a utilização da rede neural Máquina de Aprendizado Extremo (ELM) para identificação de pessoas (biometria) através do eletrocardiograma (ECG). Os dados biométricos oferecem um nível elevado de segurança para a identificação de pessoas, e o ECG é uma técnica emergente e em crescente desenvolvimento. A ELM foi pouco empregada em sistemas de reconhecimento de padrões que utilizam o sinal de ECG. Desta forma, foram estudadas as técnicas de processamento de sinal: a Transformada Wavelet e a Análise dos Componentes Principais (PCA), com o objetivo de tratar e reduzir a dimensionalidade dos dados de entrada, bem como, fazer um estudo comparativo entre a ELM e a Percepetron Múltiplas Camadas (Multilayer Perceptron – MLP). Os testes foram realizados com 90 pessoas, o sinal de ECG utilizado é referente à derivação I contendo 500 amostras/s e 12-bits de resolução dentro de uma faixa nominal de ±10mV de variação, o número de registros variou de 2 a 20 para cada pessoa. O tamanho de cada ciclo completo de ECG para o processo de formação do espaço amostral foi definido de duas formas: 167 amostras contendo as ondas P+QRS e 280 amostras contendo as ondas P+QRS+T, dos quais foram utilizados os 10 ciclos que possuíam o mais elevado nível de similaridade. Com a Transformada Wavelet, o sinal de ECG foi decomposto em 3 níveis, onde para as ondas P+QRS as reduções foram de 86, 45 e 25 amostras, e para as ondas P+QRS+T foram de 142, 73 e 39 amostras. Já para o PCA o sinal foi reduzido de 10 ciclos cardíacos para apenas 1. Estes foram apresentadas a rede formando os conjuntos de treinamento e teste. Foram utilizadas as Redes Neurais Artificiais ELM e MLP para classificação do ECG. Os resultados obtidos comprovaram que a ELM pode ser utilizada para identificação de pessoas.
This research studies the use of neural network Extreme Learning Machine (ELM) to identify individuals (biometrics) by electrocardiogram (ECG). Biometric data offer a high level of security for identifying people, and ECG is an emerging technique and increasing development. ELM was little used in pattern recognition systems that use the ECG signal. In this way, the signal processing techniques were studied: Wavelet Transform and Principal Component Analysis (PCA), with the objective of treating and reducing the dimensionality of the input data, as was as, to make a comparative study between the ELM and Multilayer Perceptron (MLP). The tests were performed with 90 people, the ECG signal used is related to the lead I containing 500 samples/s and 12- bit resolution within a nominal range of ±10 mV of variation, the number of records ranged from 2 to 20 for each people. The size of each ECG cycle to complete the process of forming the sample space defined in two ways: 167 samples containing the P+QRS waves and 280 samples containing the P+QRS+T waves, of which 10 cycles were used to had the highest level of similarity. With the Wavelet Transform, the ECG signal was decomposed into 3 levels, where for the P+QRS waves the reductions were 86, 45 and 25 samples, and for the P+QRS+T waves were 142, 73 and 39 samples. For PCA, the signal for reduced from 10 cardiac cycles to only 1. These were presented to network forming the joint training and testing. The Artificial Neural Networks ELM and MLP were used for ECG classification. The results obtained proved that the ELM may be used to identify individuals.

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Fornazin, Marcelo. „Análise de desempenho do Criptossistema Fuzzy Vault em aplicações reais /“. São José do Rio Preto : [s.n.], 2008. http://hdl.handle.net/11449/98676.

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Orientador: Marcos Antônio Cavenaghi
Banca: Ivan Rizzo Guilherme
Banca: Julio César López Hernández
Resumo: Biometria trata do reconhecimento de indivíduos baseado em características fisiológicas ou comportamentais, sendo que umas de suas aplicações é autenticação biométrica. A autenticação biométrica tem vantagens com relação às senhas, no entanto, as informações biométricas também precisam ser protegidas. Ao contrário das senhas, a biometria apresenta variabilidade no sinal, isto é, raramente duas representações de uma mesma característica biometria são idênticas, sendo que, os criptossistemas tradicionais não suportam essa propriedade. Para contornar esse problema, criptossistemas biométricos unem biometria e criptografia para encriptar informações biométricas e protegê-las. Fuzzy Vault é uma construção criptográfica que pode ser utilizada na encriptação de características biométricas e atualmente, há propostas de implementação do Fuzzy Vault para impressões digitais, íris, face e assinaturas. O presente estudo implementa o Fuzzy Vault para impressões digitais e analisa seu desempenho em um ambiente de aplicação real e em um cenário de proteção de imagens médicas usando biometria. A proposta de implementação do Fuzzy Vault encripta e desencripta o Fuzzy Vault para impressões digitais realizando o alinhamento da impressão digital. A proteção de imagens médicas encripta imagens médicas com um criptossistema tradicional e encripta a chave criptográfica com a implementação do Fuzzy Vault. Os experimentos apresentaram entre 92% e 97,96% de GAR e 0% de FAR, esses resultados ocorreram em função dos mecanismos de alinhamento e identificação de pontos candidatos implementados neste estudo. O tempo de encriptação é constante em 0,8 s. Já o tempo de desencriptação apresenta grande variabilidade, e depende da quantidade de pontos candidatos, com mediana variando entre 16 ms e 1 s nas desencriptações com sucesso... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Biometrics deals with people recognition based physiological or behavioral features where one of its application it biometric authentication. Biometric authentication has some advantages over passwords, but biometric information also need to be protected. Instead of passwords, biometrics has signal variability, i.e., two representation of the same biometric feature rarely are identical, and traditional cryptosystems does not support this feature. To overcome this issue biometric cryptosystems join biometrics and cryptography to encode biometric information and protected them. Fuzzy Vault is a cryptographic construction which can be used to encode biometric features. Today, there are some implementation proposals of Fuzzy Vault for fingerprints, iris, face and handwrite signature. This study implements Fuzzy Vault for fingerprints and analyses its performance in a real application environment and a scenario of medical image protection using biometrics. The proposed Fuzzy Vault implementation encodes and decodes Fuzzy Vault for fingerprints and performs fingerprint alignment. Medical image protecting encodes medical images with a traditional cryptosystems and encode its cryptographic key using the Fuzzy Vault implementation. Performance evaluation achieved between 92% and 97,96% of GAR and 0% of FAR, this results has been achieved with aligning and candidates points identification mechanisms implemented. Encoding time is constant in 0,8 s, but decoding time has a big variance which depends on number of candidates points, median varies between 16 ms and 1 s considering successful decodes. In the medical image protection scenario, the proposed system has a low overhead and better performance compared to a asymmetric cryptosystem, encoding time is 17 times better and decoding time is 245 times better.
Mestre

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Riera, Sardà Alexandre. „Computational Intelligence Techniques for Electro-Physiological Data Analysis“. Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/107818.

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This work contains the efforts I have made in the last years in the field of Electrophysiological data analysis. Most of the work has been done at Starlab Barcelona S.L. and part of it at the Neurodynamics Laboratory of the Department of Psychiatry and Clinical Psychobiology of the University of Barcelona. The main work deals with the analysis of electroencephalography (EEG) signals, although other signals, such as electrocardiography (ECG), electroculography (EOG) and electromiography (EMG) have also been used. Several data sets have been collected and analysed applying advanced Signal Processing techniques. On a later stage Computational Intelligence techniques, such as Machine Learning and Genetic Algorithms, have been applied, mainly to classify the different conditions from the EEG data sets. 3 applications involving EEG and classification are proposed corresponding to each one of the 3 case studies presented in this thesis. Analysis of Electrophysiological signals for biometric purposes: We demonstrate the potential of using EEG signals for biometric purposes. Using the ENOBIO EEG amplifier, and using only two frontal EEG channels, we are able to authenticate subjects with a performance up to 96.6%. We also looked for features extracted from the ECG signals and in that case the performance was equal to 97.9%. We also fused the results of both modalities achieving a perfect performance. Our system is ready to use and since it only uses 4 channels (2 for EEG, 1 for ECG in the left wrist and 1 as active reference in the right ear lobe), the wireless ENOBIO sensor is perfectly suited for our application. EEG differences in First Psychotic Episode (FPE) Patients: From an EEG data set of 15 FPE patients and the same number of controls, we studied the differences in their EEG signals in order to train a classifier able to recognise to which group an EEG sample comes from. The feature we use are extracted from the EEG by computing the Synchronization Likelihood feature between all possible pairs of channels. The next step is to build a graph and from that graph we extracted the Mean Path Length and the Clustering Coefficient. Those features as a function of the connectivity threshold are then used in our classifiers. We then create several classification problems and we reach up to 100% of classification in some cases. Markers of stress in the EEG signal: In this research, we designed a protocol in which the participants where asked to perform different tasks, each one with a different stress level. Among these tasks we can find the Stroop Test, Mathematical arithmetics and also a fake blood sample test. By extracting the alpha asymmetry and the beta/alpha ration, we where able to discriminate between the different tasks with performances up to 88%. This application can be used with only 3 EEG electrodes, and it can also work in real time. Finally this application can also be used as a neurofeedback training to learn how to cope with stress.
Este trabajo contiene los esfuerzos que he realizado en los últimos años en el campo del análisis de datos electro-fisiológicos. La mayor parte del trabajo se ha hecho en Starlab Barcelona SL y otra parte en el Laboratorio de Neurodinámica del Departamento de Psiquiatría y Psicobiología Clínica de la Universidad de Barcelona. La parte central de esta tesis está relacionado con el análisis de la señales de electroencefalografía (EEG), aunque otras señales, tales como electrocardiografía (ECG), electroculografía (EOG) y electromiografía (EMG) también se han utilizado. Varios conjuntos de datos se han recogido y analizado aplicando técnicas avanzadas de procesamiento de señales. En una fase posterior, técnicas de inteligencia computacional, tales como 'Machine Learning' y algoritmos genéticos, se han aplicado, principalmente para clasificar las diferentes condiciones de los conjuntos de datos de EEG. Las 3 aplicaciones, que involucran EEG y técnicas de clasificación, que se presentan en esta tesis son: -Análisis de señales electro-fisiológicas para aplicaciones de biometría -Diferencias en las características del EEG en pacientes de primer brote psicótico -Marcadores de estrés en la señal de EEG

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Bücher zum Thema "ECG biometry"

Lee, Jimmy Kim-Mil. ECG feature extraction without fiducial detection: Applications to ECG biometric recognition. 2006.

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Buchteile zum Thema "ECG biometry"

Mesin, Luca, Alejandro Munera und Eros Pasero. „A Low Cost ECG Biometry System Based on an Ensemble of Support Vector Machine Classifiers“. In Advances in Neural Networks, 425–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33747-0_42.

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Pal, Anita, und Yogendra Narain Singh. „ECG Biometric Recognition“. In Communications in Computer and Information Science, 61–73. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0023-3_7.

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Zheng, Gang, Shengzhen Ji, Min Dai und Ying Sun. „ECG Based Identification by Deep Learning“. In Biometric Recognition, 503–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_54.

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Zheng, Gang, Xiaoxia Sun, Shengzhen Ji, Min Dai und Ying Sun. „ECG Based Biometric by Superposition Matrix in Unrestricted Status“. In Biometric Recognition, 553–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_59.

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Dai, Min, Baowen Zhu, Gang Zheng und Yisha Wang. „A Method of ECG Identification Based on Weighted Correlation Coefficient“. In Biometric Recognition, 633–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_74.

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Saini, Sanjeev Kumar, Guru Gobind Singh und Rashmi Gupta. „ECG-Based Biometric Authentication Systems Using Artificial Intelligence Methods“. In Multimodal Biometric Systems, 61–77. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003138068-5.

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Djelouat, Hamza, Mohammed Al Disi, Abbes Amira, Faycal Bensaali und Xiaojun Zhai. „Compressive Sensing Based ECG Biometric System“. In Advances in Intelligent Systems and Computing, 126–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_11.

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Sun, Yanwen, Gongping Yang, Yuwen Huang, Kuikui Wang und Yilong Yin. „Learning Discriminative Representation for ECG Biometrics Based on Multi-Scale 1D-PDV“. In Biometric Recognition, 415–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31456-9_46.

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Srivastva, Ranjeet, und Yogendra Narain Singh. „ECG Biometric Analysis Using Walsh–Hadamard Transform“. In Advances in Data and Information Sciences, 201–10. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8360-0_19.

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Gupta, Ashish, Rajesh Kumar und Devvrat Tyagi. „Wireless Sensor Network for IoT-Based ECG Monitoring System Using NRF and LabVIEW“. In Multimodal Biometric Systems, 125–34. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003138068-10.

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Konferenzberichte zum Thema "ECG biometry"

Meltzer, David, und David Luengo. „Fiducial ECG-Based Biometry: Comparison of Classifiers and Dimensionality Reduction Methods“. In 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2019. http://dx.doi.org/10.1109/tsp.2019.8768891.

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Bashar, Md Khayrul, Yuji Ohta und Hiroaki Yoshida. „ECG-based biometric authentication using mulscale descriptors: ECG-based biometric authentication“. In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2015. http://dx.doi.org/10.1109/iciibms.2015.7439465.

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Sanchez-Casanova, Jorge, Antonio Miranda-Escalada, Raul Sanchez-Reillo und Pablo Bartolome-Molina. „ECG biosignals in biometric recognition“. In 2017 International Carnahan Conference on Security Technology (ICCST). IEEE, 2017. http://dx.doi.org/10.1109/ccst.2017.8167817.

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Carvalho, João, Susana Brás und Armando Pinho. „Entropy-Based ECG Biometric Identification“. In Entropy 2021: The Scientific Tool of the 21st Century. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/entropy2021-09795.

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Plataniotis, Konstantinos N., Dimitrios Hatzinakos und Jimmy K. M. Lee. „ECG Biometric Recognition Without Fiducial Detection“. In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341628.

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Tantawi, M., A. Salem und M. F. Tolba. „ECG signals analysis for biometric recognition“. In 2014 14th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2014. http://dx.doi.org/10.1109/his.2014.7086192.

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Fatimah, Binish, G. Priyanka, Rehana Sultana und N. Rekha. „Analysis of ECG for biometric identification“. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225361.

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Safie, S. I. „ECG slope features for Biometric Authentication“. In 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). IEEE, 2018. http://dx.doi.org/10.1109/icsima.2018.8688793.

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Wang, Kuikui, Gongping Yang, Lu Yang, Yuwen Huang und Yilong Yin. „STERLING: Towards Effective ECG Biometric Recognition“. In 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021. http://dx.doi.org/10.1109/ijcb52358.2021.9484360.

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Loong, Justin Leo Cheang, Sim Kok Swee, Rosli Bear, Khazaimatol S. Subari und Muhammad Kamil Abdullah. „Effects of diseased ECG on the robustness of ECG biometric systems“. In 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2010. http://dx.doi.org/10.1109/iecbes.2010.5742250.

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Berichte der Organisationen zum Thema "ECG biometry"

Watson, Craig I., Michael D. Garris, Elham Tabassi, Charles L. Wilson, R. Michael McCabe, Stanley Janet und Kenneth Ko. User's guide to export controlled distribution of NIST biometric image software (NBIS-EC). Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7391.

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