Continuous multibiometric authentication for online exam with machine learning

Multibiometric authentication has been received great attention over the past decades with the growing demand of a robust authentication system. Continuous authentication system verifies a user continuously once a person is login in order to prevent intruders from the impersonation. In this study, we propose a continuous multibiometric authentication system for the identification of the person during online exam using two modalities, face recognition and keystrokes. Each modality is separately processed to generate matching scores, and the fusion method is performed at the score level to improve the accuracy. The EigenFace and support vector machine (SVM) approach are applied to the facial recognition and keystrokes dynamic accordingly. The matching score calculated from each modality is combined using the classification by the decision tree with the weighted sum after the score is split into three zones of interes

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

E-INVIGILATION OF E-ASSESSMENTS

E-learning and particularly distance-based learning is becoming an increasingly important mechanism for education. A leading Virtual Learning Environment (VLE) reports a user base of 70 million students and 1.2 million teachers across 7.5 million courses. Whilst e-learning has introduced flexibility and remote/distance-based learning, there are still aspects of course delivery that rely upon traditional approaches. The most significant of these is examinations. The lack of being able to provide invigilation in a remote-mode has restricted the types of assessments, with exams or in-class test assessments proving difficult to validate. Students are still required to attend physical testing centres in order to ensure strict examination conditions are applied. Whilst research has begun to propose solutions in this respect, they fundamentally fail to provide the integrity required. This thesis seeks to research and develop an e-invigilator that will provide continuous and transparent invigilation of the individual undertaking an electronic based exam or test. The analysis of the e-invigilation solutions has shown that the suggested approaches to minimise cheating behaviours during the online test have varied. They have suffered from a wide range of weaknesses and lacked an implementation achieving continuous and transparent authentication with appropriate security restrictions. To this end, the most transparent biometric approaches are identified to be incorporated in an appropriate solution whilst maintaining security beyond the point-of-entry. Given the existing issues of intrusiveness and point-of-entry user authentication, a complete architecture has been developed based upon maintaining student convenience but providing effective identity verification throughout the test, rather than merely at the beginning. It also provides continuous system-level monitoring to prevent cheating, as well as a variety of management-level functionalities for creating and managing assessments including a prioritised and usable interface in order to enable the academics to quickly verify and check cases of possible cheating. The research includes a detailed discussion of the architecture requirements, components, and complete design to be the core of the system which captures, processes, and monitors students in a completely controlled e-test environment. In order to highlight the ease of use and lightweight nature of the system, a prototype was developed. Employing student face recognition as the most transparent multimodal (2D and 3D modes) biometrics, and novel security features through eye tracking, head movements, speech recognition, and multiple faces detection in order to enable a robust and flexible e-invigilation approach. Therefore, an experiment (Experiment 1) has been conducted utilising the developed prototype involving 51 participants. In this experiment, the focus has been mainly upon the usability of the system under normal use. The FRR of those 51 legitimate participants was 0 for every participant in the 2D mode; however, it was 0 for 45 of them and less than 0.096 for the rest 6 in the 3D mode. Consequently, for all the 51 participants of this experiment, on average, the FRR was 0 in 2D facial recognition mode, however, in 3D facial recognition mode, it was 0.048. Furthermore, in order to evaluate the robustness of the approach against targeted misuse 3 participants were tasked with a series of scenarios that map to typical misuse (Experiment 2). The FAR was 0.038 in the 2D mode and 0 in the 3D mode. The results of both experiments support the feasibility, security, and applicability of the suggested system. Finally, a series of scenario-based evaluations, involving the three separate stakeholders namely: Experts, Academics (qualitative-based surveys) and Students (a quantitative-based and qualitative-based survey) have also been utilised to provide a comprehensive evaluation into the effectiveness of the proposed approach. The vast majority of the interview/feedback outcomes can be considered as positive, constructive and valuable. The respondents agree with the idea of continuous and transparent authentication in e-assessments as it is vital for ensuring solid and convenient security beyond the point-of-entry. The outcomes have also supported the feasibility and practicality of the approach, as well as the efficiency of the system management via well-designed and smart interfaces.The Higher Committee for Education Development in Iraq (HCED

Análise de propriedades intrínsecas e extrínsecas de amostras biométricas para detecção de ataques de apresentação

Orientadores: Anderson de Rezende Rocha, Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os recentes avanços nas áreas de pesquisa em biometria, forense e segurança da informação trouxeram importantes melhorias na eficácia dos sistemas de reconhecimento biométricos. No entanto, um desafio ainda em aberto é a vulnerabilidade de tais sistemas contra ataques de apresentação, nos quais os usuários impostores criam amostras sintéticas, a partir das informações biométricas originais de um usuário legítimo, e as apresentam ao sensor de aquisição procurando se autenticar como um usuário válido. Dependendo da modalidade biométrica, os tipos de ataque variam de acordo com o tipo de material usado para construir as amostras sintéticas. Por exemplo, em biometria facial, uma tentativa de ataque é caracterizada quando um usuário impostor apresenta ao sensor de aquisição uma fotografia, um vídeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em íris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de íris de um usuário-alvo ou mesmo padrões de textura sintéticas. Nos sistemas biométricos de impressão digital, os usuários impostores podem enganar o sensor biométrico usando réplicas dos padrões de impressão digital construídas com materiais sintéticos, como látex, massa de modelar, silicone, entre outros. Esta pesquisa teve como objetivo o desenvolvimento de soluções para detecção de ataques de apresentação considerando os sistemas biométricos faciais, de íris e de impressão digital. As linhas de investigação apresentadas nesta tese incluem o desenvolvimento de representações baseadas nas informações espaciais, temporais e espectrais da assinatura de ruído; em propriedades intrínsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de características. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponível contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuínos em um sistema biométrico facial, os quais foram coletados com a autorização do Comitê de Ética em Pesquisa da Unicamp; o desenvolvimento de novas abordagens para modelagem e análise de propriedades extrínsecas das amostras biométricas relacionadas aos artefatos que são adicionados durante a fabricação das amostras sintéticas e sua captura pelo sensor de aquisição, cujos resultados de desempenho foram superiores a diversos métodos propostos na literature que se utilizam de métodos tradicionais de análise de images (e.g., análise de textura); a investigação de uma abordagem baseada na análise de propriedades intrínsecas das faces, estimadas a partir da informação de sombras presentes em sua superfície; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de características relacionadas ao nosso problema, cujos resultados foram superiores ou competitivos aos métodos considerados estado da arte para as diferentes modalidades biométricas consideradas nesta tese. A pesquisa também considerou o projeto de eficientes redes neurais com arquiteturas rasas capazes de aprender características relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponíveis para o desenvolvimento e a avaliação de soluções para a detecção de ataques de apresentaçãoAbstract: Recent advances in biometrics, information forensics, and security have improved the recognition effectiveness of biometric systems. However, an ever-growing challenge is the vulnerability of such systems against presentation attacks, in which impostor users create synthetic samples from the original biometric information of a legitimate user and show them to the acquisition sensor seeking to authenticate themselves as legitimate users. Depending on the trait used by the biometric authentication, the attack types vary with the type of material used to build the synthetic samples. For instance, in facial biometric systems, an attempted attack is characterized by the type of material the impostor uses such as a photograph, a digital video, or a 3D mask with the facial information of a target user. In iris-based biometrics, presentation attacks can be accomplished with printout photographs or with contact lenses containing the iris patterns of a target user or even synthetic texture patterns. In fingerprint biometric systems, impostor users can deceive the authentication process using replicas of the fingerprint patterns built with synthetic materials such as latex, play-doh, silicone, among others. This research aimed at developing presentation attack detection (PAD) solutions whose objective is to detect attempted attacks considering different attack types, in each modality. The lines of investigation presented in this thesis aimed at devising and developing representations based on spatial, temporal and spectral information from noise signature, intrinsic properties of the biometric data (e.g., albedo, reflectance, and depth maps), and supervised feature learning techniques, taking into account different testing scenarios including cross-sensor, intra-, and inter-dataset scenarios. The main findings and contributions presented in this thesis include: the creation of a large and publicly available benchmark containing 17K videos of presentation attacks and bona-fide presentations simulations in a facial biometric system, whose collect were formally authorized by the Research Ethics Committee at Unicamp; the development of novel approaches to modeling and analysis of extrinsic properties of biometric samples related to artifacts added during the manufacturing of the synthetic samples and their capture by the acquisition sensor, whose results were superior to several approaches published in the literature that use traditional methods for image analysis (e.g., texture-based analysis); the investigation of an approach based on the analysis of intrinsic properties of faces, estimated from the information of shadows present on their surface; and the investigation of different approaches to automatically learning representations related to our problem, whose results were superior or competitive to state-of-the-art methods for the biometric modalities considered in this thesis. We also considered in this research the design of efficient neural networks with shallow architectures capable of learning characteristics related to our problem from small sets of data available to develop and evaluate PAD solutionsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação140069/2016-0 CNPq, 142110/2017-5CAPESCNP

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Biopotential signals and their applicability to cibersecurity problems

Biometric systems are an uprising technique of identification in today’s world. Many different biometric systems have been used in everyone’s daily life in the past years, such as fingerprint, face scan, ECG, and others. More than 20 years evince that the Elektrokardiogramm (EKG) or Electrocardiogram (ECG) is a feasible method to perform user identification as each person has their unique and inherent Elektrokardiogramm (EKG). A biometric system is based on something that every human being is and cannot lose or possess as it is an eye, the DNA, palm print, vein patterns, iris, retina, etc. For this reason, during the last decade, biometric identification or authentication has gained ground between the classic authentication systems as it was a PIN or a physical key. All biometric systems, to be accepted, must fulfill a set of requirements including universality, uniqueness, permanence, and collectability. The EKG is a biometric trait that not only fulfills those requirements but also has some advantages over other biometric traits. To use an EKG as the biometric trait for identification is motivated by four key points: 1) the collection of an EKG is a non-invasive technique so may contribute to the acceptability among the population; 2) a human being can only be identified if they are alive as their heart must be beating; 3) all living beings have their EKG so it is inclusive; 4) an EKG not only provides identification but also provides a medical and even emotional diagnose. There exist many works regarding user identification with EKGs in the current state-of-the-art. Biometric identification with EKGs has been deployed using many different techniques. Some works use the fiducial points of the EKG signal (T-peak, R-peak, P-onset, QRS-offset, ...) to perform the user identification and others use feature extraction performed by a Neural Network as the classification or identification method. As the EKG is a signal which is expressed in time and frequency, many different Neural Network models can exploit the dissimilarity between each EKG signal from each user to perform user identification such as Recurrent Neural Networks, Convolutional Neural Networks, Long-Short Term Memory, Principal Component Analysis, among others offering very competitive results. Focusing on user identification, depending on the user condition in each case, as has been commented before, the EKG not only contributes as an identification method but also offers a diagnosis as it is a person’s condition from a medical point of view or a person’s status regarding their emotional state. Some research has studied certain conditions such as anxiety over EKG identification showing that higher heart rates might be more complex to identify individuals. Nevertheless, there are some drawbacks in the current state-of-the-art regarding identification with EKG. Many systems use very complexly Deep Learning architectures or, as commented, extract the features by a fiducial analysis making the biometric system too complex and computationally costly. One important flaw, not only in biometric systems but in science, is the lack of publicly available datasets and the use of private ones to perform different studies. Using a private database for any research makes the experiments and results irreproducible and it could be considered a drawback in any science field. Furthermore, many of these works use the EKG signal in a sense that it can be recovered from the identification system so there is no privacy protection for the user as anyone could retrieve their EKG signal. Owing to the many drawbacks of a biometric system based on ECG signals, ELEKTRA is presented in this thesis as a new identification system whose aim is to overcome all the inconveniences of the current proposals. ELEKTRA is a biometric system that performs user identification by using EKGs converted into a heatmap of a set of aligned R-peaks (heartbeats), forming a matrix called an Elektrokardiomatrix (EKM). ELEKTRA is based on past work where the EKM was already created for medical purposes. As far as the literature covers up to this date, all the existing research regarding the use of the EKM is focused on the diagnosis of different Cardiovascular Disease (CVD) such as Congestive Heart Failure, Atrial Fibrillation, and Heart Rate Variability, among others. Therefore, the work presented in this thesis, presumably, is the first one to use the EKM as a valid identification method. In aim to offer reproducible results, four different public databases are taken to show the model feasibility and adaptability: i) the Normal Sinus Rhythm Database (NSRDB), ii) the MIT-BIH Arrhythmia Database (MITBIHDB), iii) the Physikalisch-Technische Bundesanstalt (PTBDB), and iv) the Glasgow University Database (GUDB). The first three of them (i, ii and iii) are taken from Physionet a freely-available repository with medical research data, managed by the MIT Laboratory. However, the fourth database (iv ) is also freely available by petition to Glasgow University. Furthermore, to test ELEKTRA’s adaptability and feasibility of the biometric system presented, four different datasets are built from the databases where the EKG signals are segmented into windows to create several Elektrokardiomatrix (EKM)s. The number of EKMs built for each dataset will depend on the length of the records. For example, for the Normal Sinus Rhythm Database (NSRDB) as the EKG records are very extensive, 3000 EKMs or images per user will be obtained. However, for the three other databases, the highest possible number of EKM images is obtained until the signal is lost. It is important to take into account that depending on the number of heartbeats taken to be represented in each EKM, a different number of EKMs is obtained for the three databases in which EKG recordings are shorter. As higher the number of heartbeats o R-peaks taken (i.e., 7bpf), the fewer images will be obtained. Once the datasets of EKMs are constructed, a simple yet effective Convolutional Neural Network (CNN) is built by one 2D Convolution with ReLU activation, a max-pooling operation followed by a dropout to include regularisation and, and finally, a layer with flattened and dense operations with a softmax or sigmoid function depending if the classification task is categorical o binary to achieve the final classification. With this simple CNN, the feasibility and adaptability of ELEKTRA are demonstrated during all the experiments. The four databases are tested during chapters 3, 4, and 5 where the experimentation takes place. In Chapter 3, the NSRDB is studied as the baseline of identification with control users. Different experiments are conducted with aim of studying ELEKTRA’s behavior. In the first experiments, how many heartbeats are needed to identify a user and the costs of convergence of the model depending on the time computing and the number of heartbeats taken to be represented in the EKM are studied. In this case, similar results are achieved in all the experiments as results close to 100% of accuracy are obtained. In the classification of a non-seen user a user, from a different database that has not been seen in any other experiment, is processed and tested against the network. The result obtained is that a non-seen user or an impersonator would only bypass the system one in ten times which can be considered a low ratio when many systems are blocked after three to five attempts. The classification of a user is tested to have a closer situation in which a low-cost sensor is used. For this experiment, an EKG signal is modified by adding Gaussian noise and then processed as any other signal. As a demonstration of our robust system, an accuracy of 99% is obtained indicating that a noisy signal can be processed too. The last experiment over the NSRDB is where this database is used to test the feasibility of ELEKTRA by testing how many images or EKM are enough to identify a user. Even though there is a decrease in accuracy when the number of images used to train the network is decreased too, a 97% of accuracy is obtained when training the network with only 300 EKMs per user. This chapter concludes that, as shown in all the experiments, ELEKTRA is a valid and feasible identification method for control users. The MIT-BIH Arrhythmia Database (MIT-BIHDB) is a database comprising patients with Arrhythmia and random users, and the Physikalisch- Technische Bundesanstalt (PTBDB) comprises patients with different CVD together with healthy users. Hence, the main goal in Chapter 4 is to study the identification system proposed over users with CVD showing ELEKTRA’s adaptability. First of all, the MIT-BIHDB is tested achieving outperforming results and showing how ELEKTRokardiomatrix Application to biometric identification with Convolutional Neural Networks (ELEKTRA) is capable to identify a pool of users with and without arrhythmia with just a slight decrease of the network’s accuracy as a 97% of accuracy is obtained. Secondly, the whole PTBDB is taken to test the biometric system. The result obtained in this experiment is lower than in the other ones (a 93% of accuracy) as the number of images used to train the network has suffered a great decrease compared to the other experiments and 232 users are being studied. Lastly, ELEKTRA has tested over 162 users from the PTBDB with specific CVD which, namely, are Bundle branch block, Cardiomyopathy, Dysrhythmia, Myocardial infarction, Myocarditis, and Valvular heart disease. Through this experiment, the aim is to see ELEKTRA’s behaviour when only users with CVD are included. Better results are obtained compared to the last experiment. It can be owed that the number of users has decreased and that a CVD makes more unique each EKG as many researchers use the EKM for diagnosis purposes. The conclusion extracted from all the experiments from this chapter is that ELEKTRA is capable to identify users with and without CVD approaching a real-life scenario. In Chapter 5 the Glasgow University Database (GUDB) is tested to evaluate the performance of user identification when the users are performing different activities. The GUDB comprises 25 users performing five different activities with different levels of cardiovascular effort: sitting, walking on a treadmill, doing a maths exam, using a handbike, and running on a treadmill. The proposed biometric system is tested with each of these activities for 3 and 5 bpf achieving different results in each case. For the experiments performed where an activity requiring lower cardiovascular effort such as sitting or walking, the accuracy obtained is close to 100% as it is 99.19% for sitting and 98.59% for walking. Then for the scenarios where higher heartbeat rates are supposed the experiment results in lower accuracies as it is jogging with an 82.63% and biking with a 95.51%. For the maths scenario, its outcome is different; the heartbeat rate for each user could be different depending on how nervous each user is. Hence, a 94.0% is obtained with this activity. The conclusion extracted from these first experiments is that it is more complex to identify users when they are performing an activity that requires a higher cardiovascular effort and, consequently have a higher heart rate. For the following experiment, all scenarios have been merged to study the behaviour of a system that has been trained with users performing different activities. In this case, the results obtained seemed to be close to the mean of the results obtained before as the general accuracy for all the scenarios with 3bpf is 91.32%. For the subsequent experiments, some of the scenarios have been merged into two different categories. On the one hand, the more calmed activities (sitting and walking) have been merged in the so-called Low Cardiovascular Activity (LCA) scenario. The accuracy obtained by training and testing with these two activities together is 97.74% and an EER of 1.01%. On the other hand, the High Cardiovascular Activity (HCA) scenario is composed by activities that require a higher cardiovascular effort (jogging and biking). In this case, the results obtained have decreased compared to the last ones as the accuracy is 85.71%. It can be noticed that what has suffered a considerable increase is the False Rejection Rate (FRR) which is 14.17% without implying an increase in the False Acceptance Rate (FAR) which is still very low as it is 0.6%. The last experiments have been called fight of scenarios as there is a confrontation between scenarios by merging some of them and training with some activities or scenarios and predicting with different ones. The first experiments that can be found in this section are training with the LCA group and testing with the HCA group and vice versa. The results here show a great decrease in the performance as accuracies are 37.24% and 46.42%, respectively. This fact implies that it is more complex to identify users that have been registered with a different heartbeat rate. Last but not least, there are a set of experiments where the activities have been confronted such as training the network with the sitting scenario and testing with the jogging scenario. These experiments confirm the hypothesis for higher heart rates, are more complex to identify users, and even more when the network has been trained over calmed users. Even though, one of the main advantages of the presented model is that, even for low accuracies, the False Acceptance Rate has not increased compared to the other experiments meaning that an impostor could not achieve bypassing the system. Lastly, in Chapter 6 conclusions and discussions are offered. A comparison between ELEKTRA and other biometric systems based on EKGs from the current state-of-the-art is offered. These researches from the literature are examined to show how ELEKTRA outperforms all of them in regards to some of the aspects such as efficiency, complexity, accuracy, error rates, and reproducibility among others. It is important to remark that, compared to the other works, in all experiments performed in this doctoral thesis, really high performances with high accuracies and low error rates are achieved. In fact, what is remarkable is that this performance is obtained using a very simple CNN conformed by just one convolutional layer. By achieving outstanding results with a simple neural network, the solidity of ELEKTRA is proven. By this, ELEKTRA contributes to the state-of-the-art by providing a new method for user identification with EKGs with many benefits. Outstanding results in terms of high accuracy and low error rates in the experiments assure the efficiency of ELEKTRA. The fact that the databases used to perform the experimentation in this doctoral thesis are publicly available, makes this work reproducible in contrast to many other works in the literature. In fact, as the databases used are different depending on the users’ nature conforming to each database, it is established that the identification method proposed is inclusive as all living beings have their own EKG and high accuracies are also obtained when testing the model over users with different CVD. Moreover, as it has been proven that users with CVD can also be identified without having major drawbacks, ELEKTRA offers an identification system that can also offer a diagnosis of the user who is being identified in terms of their medical health. In addition, thanks to the GUDB, ELEKTRA can determine for the first time, as far as the literature reaches, that performing user identification with EKGs over users performing activities requiring a higher cardiovascular effort and consequently having higher heartbeat rates, is more complex. In conclusion, by the studies and experiments performed in this doctoral thesis, it can be assumed that ELEKTRA is a feasible and efficient identification method for biometrics with EKG and outperforms the current stateof- the-art proposals in user identification with EKG.Los sistemas biométricos son una técnica de identificación en auge en la actualidad. En los últimos años se han utilizado muchos sistemas diferentes en la vida cotidiana, como la huella dactilar, el escáner facial, o el ECG, entre otros. De hecho, son más de 20 años los que avalan que el Elektrokardiogramm (EKG) o el Electrocardiogram (ECG) es un método fiable para realizar identificación de usuarios. En esta tesis se propone un nuevo método de identificación biométrica denominado ELEKTRA. Por otro lado, existen algunos inconvenientes en el estado del arte actual respecto a la identificación con EKG. Muchos sistemas utilizan arquitecturas muy complejas de Deep Learning o extraen las características importantes mediante un análisis fiduciario, haciendo que el sistema biométrico sea demasiado complejo o costoso. Un fallo importante, no solo en los sistemas biométricos, es la falta de bases de datos públicas y el uso de bases de datos privadas para la investigación. El uso de bases de datos privadas en cualquier estudio hace que los experimentos y los resultados sean irreproducibles y son un inconveniente en cualquier campo de la ciencia. En esta tesis doctoral se ha desarrollado ELEKTRA, un sistema de identificación biométrica, mediante el uso de imagénes llamadas Elektrokardiomatrix (EKM). Estas imágenes se construyen a partir de realizar un mapa de calor de un conjunto de picos R (latidos) alineados, formando una matriz. Con el fin de ofrecer resultados reproducibles, se usan cuatro diferentes bases de datos públicas para demostrar la viabilidad y adaptabilidad del modelo: la Normal Sinus Rhythm Database (NSRDB), la MIT-BIH Arrhythmia Database (MIT-BIHDB), la Physikalisch-Technische Bundesanstalt (PTBDB) y la Glasgow University Database (GUDB). Se han creado nuevas sub-bases de datos de EKMs a partir de cada una de las bases de datos mencionadas. Además, para testear la adaptabilidad y viabilidad de ELEKTRA como sistema biométrico se construye una CNN sencilla, pero eficaz, con una sola capa Convolucional. Las cuatro bases de datos anteriormente mencionadas se han testeado en los Capítulos 3, 4 y 5. En el Capítulo 3 se estudia la NSRDB como prueba de concepto de identificación en usuarios control. Se realizan diferentes experimentos con el objetivo de estudiar el comportamiento de ELEKTRA. Las características estudiadas con esta base de datos son: cuántos latidos son necesarios para identificar a un usuario; los costes de convergencia del modelo presentado; la clasificación de un usuario jamás visto proveniente de una base de datos diferente; la clasificación de un usuario cuya señal EKG ha sido modificada añadiendo ruido Gaussiano; y la viabilidad de ELEKTRA probando cuántas imágenes o EKM son suficientes para identificar a un usuario. En cuanto a las bases de datos que contienen usuarios con CVD, la MITBIHDB contiene pacientes con Arritmia y usuarios sanos, y la PTBDB contiene pacientes con diferentes CVD junto a usuarios sanos. Estas dos bases de datos se estudian en el Capítulo 4, donde se estudia la adaptabilidad de ELEKTRA a distintas CVDs. En primer lugar, se testea la MIT-BIHDB logrando resultados prometedores y mostrando cómo ELEKTRA es capaz de identificar usuarios con y sin arritmia en el mismo grupo. En segundo lugar, se toma la PTBDB completa obteniendo porcentajes altos de acierto y bajos en cuanto a tasas de error concierne. Y por último, se prueba ELEKTRA sobre algunos usuarios con CVD específicos de la PTBDB para ver su comportamiento cuando sólo se incluyen usuarios con CVD. El resultado de estos experimentos muestra cómo ELEKTRA es capaz de identificar a los usuarios con y sin CVD acercándose a un escenario real. Por último, en el capítulo 5 se prueba ELEKTRA sobre la GUDB para evaluar el rendimiento de la identificación de usuarios cuando éstos realizan diferentes actividades cardiovasculares. La GUDB consta de 25 usuarios que realizan cinco actividades diferentes con distintos niveles de esfuerzo cardiovascular (sentarse, caminar, hacer un examen de matemáticas, usar una bicicleta de mano y correr en una cinta). El sistema biométrico propuesto se prueba con cada una de estas actividades para mostrar que es más complejo identificar a los usuarios cuando realizan una actividad que requiere un mayor esfuerzo cardiovascular y, en consecuencia, tienen una mayor frecuencia cardíaca. Los experimentos realizados consisten en fusionar diferentes actividades para estudiar las diferencias entre las frecuencias cardíacas y cómo la identificación del usuario está relacionada la misma. El experimento más representativo se realiza entrenando el modelo con el escenario en el que el usuario está sentado y realizando la clasificación ciega de usuarios del escenario en el cual están corriendo. En este experimento, se obtiene una precisión realmente baja demostrando que para frecuencias de latidos más altas es más complejo identificar a un usuario. De hecho, una de las principales ventajas del modelo presentado es que, incluso con una precisión baja, la Tasa de Falsa Aceptación no ha aumentado en comparación con los otros experimentos, lo que significa que un impostor no podría conseguir eludir el sistema. Sin embargo, si la base de datos se lanza sobre todas las actividades fusionadas, se muestran resultados precisos que ofrecen un modelo inclusivo para entrenar y probar sobre usuarios que realizan diferentes actividades. De este modo, ELEKTRA contribuye al estado del arte proporcionando un nuevo método de identificac