Development of new techniques for the recovery of conductive fingermark

Fingerprints are an important type of evidence within the practice of forensic investigation and are growing in importance in terms of security. Fingerprints as evidence are one of the most highly regarded forms of evidence in court. The uniqueness of fingerprints and the admissibility of such evidence has made fingerprints a vital part of forensic investigation. This being said the techniques used for recovering such evidence have not been developed much since the first uses in the 19th and 20th centuries. The modern-day role of fingerprints is becoming more apparent in technology and biosecurity, but this role has not yet been considered when recovering fingerprints throughout a crime. In order to develop a recovery technique that would allow application within technology, a level of conductivity is required to activate many of the sensors used in order to strengthen the level of security. This research highlights there is a way of developing existing techniques implemented within forensic investigation in a way that will consider this technological application. By finding a material that will conduct the current from a human body and capture the details of a fingerprint, a device may be unlocked by someone other than the electronic devices user. The success of this across various surfaces and device types could lead to the development of standard practices within forensic investigation, allowing the uses of such recovered fingermarks to be used more routinely throughout crime scene investigations

Usability in biometric recognition systems

Mención Internacional en el título de doctorBiometric recognition, which is a technology already mature, grows nowadays in several contexts, including forensics, access controls, home automation systems, internet, etc. Now that technology is moving to mobile scenarios, biometric recognition is being also integrated in smartphones, tablets and other mobile devices as a convenient solution for guaranteeing security, complementing other methods such as PIN or passwords. Nevertheless, the use of biometric recognition is not as spread as desired and it is still unknown for a wide percentage of the population. It has been demonstrated [1] that some of the possible reasons for the slow penetration of biometrics could be related to usability concerns. This could lead to various drawbacks like worst error rates due to systems misuses and it could end with users rejecting the technology and preferring other approaches. This Thesis is intended to cover this topic including a study of the current state of the art, several experiments analysing the most relevant usability factors and modifications to a usability evaluation methodology. The chosen methodology is the H-B interaction, carried out by Fernandez-Saavedra [2], based on the ISO/IEC 19795 [3], the HBSI [4], the ISO 9241-210 [5] and on Common Criteria [6]. Furthermore, this work is focused on dealing with accessibility concerns in biometric recognition systems. This topic, usually included into the usability field, has been addressed here separately, though the study of the accessibility has followed the same steps as the usability study: reviewing the state of the art, pointing and analysing the main influential factors and making improvements to the state of the art. The recently published standard EN 301 549 – “Accessibility requirements suitable for public procurement of ICT products and services in Europe” [7] has been also analysed. These two topics have been overcome through the well-known user-centric-design approach. In this way, first the influential factors have been detected. Then, they have been isolated (when possible) and measured. The results obtained have been then interpreted to suggest new updates to the H-B interaction. This 3-steps approach has been applied cyclically and the factors and methodology updated after each iteration. Due to technology and usability trends, during this work, all the systems/applications developed in the experiments have been thought to be mobile directly or indirectly. The biometric modalities used during the experiments performed in this Thesis are those pointed as suitable for biometric recognition in mobile devices: handwritten recognition signature, face and fingerprint recognition. Also, the scenarios and the applications used are in line with the main uses of biometrics in mobile environments, such as sign documents, locking/unlocking devices, or make payments. The outcomes of this Thesis are intended to guide future developers in the way of designing and testing proper usable and accessible biometrics. Finally, the results of this Thesis are being suggested as a new International Standard within ISO/IEC/JTC1/SC37 – Biometric Recognition, as standardization is the proper way of guaranteeing usability and accessibility in future biometric systems. The contributions of this Thesis include: • Improvements to the H-B interaction methodology, including several usability evaluations. • Improvements on the accessibility of the ICT (Information and Communications Technology) products by means of the integration of biometric recognition systems • Adaptation and application of the EN 301 549 to biometric recognition systems.El reconocimiento biométrico, que es una tecnología ya madura, crece hoy en día en varios contextos, incluyendo la medicina forense, controles de acceso, sistemas de automatización del hogar, internet, etc. Ahora que la tecnología se está moviendo a los escenarios móviles, el reconocimiento biométrico está siendo también integrado en los teléfonos inteligentes, tabletas y otros dispositivos móviles como una solución conveniente para garantizar la seguridad, como complemento de otros métodos de seguridad como el PIN o las contraseñas. Sin embargo, el uso del reconocimiento biométrico es todavía desconocido para un amplio porcentaje de la población. Se ha demostrado [1] que algunas de las posibles razones de la lenta penetración de la biometría podrían estar relacionadas con problemas de usabilidad. Esto podría dar lugar a diversos inconvenientes, ofreciendo un rendimiento por debajo de lo esperado debido al mal uso de los sistemas y podría terminar con los usuarios rechazando la tecnología y prefiriendo otros enfoques. Esta tesis doctoral trata este tema incluyendo un estudio del estado actual de la técnica, varios experimentos que analizan los factores de usabilidad más relevantes y modificaciones a una metodología de evaluación de la usabilidad, la "H-B interaction" [2] basada en la ISO / IEC 19795 [3], el HBSI [4], la ISO 9241 [5] y Common Criteria [6]. Además, este trabajo se centra también en los problemas de accesibilidad de los sistemas de reconocimiento biométrico. Este tema, que por lo general se incluye en el campo de la usabilidad, se ha tratado aquí por separado, aunque el estudio de la accesibilidad ha seguido los mismos pasos que el estudio de usabilidad: revisión del estado del arte, análisis de los principales factores influyentes y propuesta de cambios en la metodología H-B interaction. Han sido también analizados los requisitos de accesibilidad para las Tecnologías de la Información y la Comunicación (TIC) en Europa, bajo la norma EN 301 549 [7]. Estos dos temas han sido estudiados a través de un enfoque centrado en el usuario (User Centric Design - UCD). De esta manera, se han detectado los factores influyentes. A continuación, dichos factores han sido aislados (cuando ha sido posible) y medidos. Los resultados obtenidos han sido interpretados para sugerir nuevos cambios a la metodología H-B interaction. Este enfoque de 3 pasos se ha aplicado de forma cíclica a los factores y a la metodología después de cada iteración. Debido a las tendencias tecnológicas y de usabilidad, durante este trabajo, todos los sistemas / aplicaciones desarrolladas en los experimentos se han pensado para ser móviles, directa o indirectamente. Las modalidades utilizadas durante los experimentos realizados en esta tesis doctoral son las que se señalaron como adecuados para el reconocimiento biométrico en dispositivos móviles: la firma manuscrita, la cara y el reconocimiento de huellas dactilares. Además, los escenarios y las aplicaciones utilizadas están en línea con los principales usos de la biometría en entornos móviles, como la firma de documentos, el bloqueo / desbloqueo de dispositivos, o hacer pagos. Los resultados de esta tesis tienen como objetivo orientar a los futuros desarrolladores en el diseño y evaluación de la usabilidad y la accesibilidad en los sistemas de reconocimiento biométrico. Por último, los resultados de esta tesis doctoral se sugerirán como un nuevo estándar de ISO / IEC / JTC1 / SC37 - Biometric Recognition, ya que la normalización es la manera adecuada de garantizar la usabilidad y la accesibilidad en los futuros sistemas biométricos. Las contribuciones de esta tesis incluyen: • Mejora de la metodología de evaluación H-B interaction, incluyendo varias evaluaciones de usabilidad. • Mejora de la accesibilidad de los sistemas de información / electrónicos mediante la integración de sistemas biométricos y varias evaluaciones. • Adaptación y aplicación de la norma de accesibilidad EN 301 549 al campo de los sistemas biométricos.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Patrizio Campisi.- Secretario: Enrique Cabellos Pardo.- Vocal: Marcos Faundez Zanu

Biometrics & [and] Security:Combining Fingerprints, Smart Cards and Cryptography

Since the beginning of this brand new century, and especially since the 2001 Sept 11 events in the U.S, several biometric technologies are considered mature enough to be a new tool for security. Generally associated to a personal device for privacy protection, biometric references are stored in secured electronic devices such as smart cards, and systems are using cryptographic tools to communicate with the smart card and securely exchange biometric data. After a general introduction about biometrics, smart cards and cryptography, a second part will introduce our work with fake finger attacks on fingerprint sensors and tests done with different materials. The third part will present our approach for a lightweight fingerprint recognition algorithm for smart cards. The fourth part will detail security protocols used in different applications such as Personal Identity Verification cards. We will discuss our implementation such as the one we developed for the NIST to be used in PIV smart cards. Finally, a fifth part will address Cryptography-Biometrics interaction. We will highlight the antagonism between Cryptography – determinism, stable data – and Biometrics – statistical, error-prone –. Then we will present our application of challenge-response protocol to biometric data for easing the fingerprint recognition process

Device profiling analysis in Device-Aware Network

As more and more devices with a variety of capabilities are Internet-capable, device independence becomes a big issue when we would like the information that we request to be correctly displayed. This thesis introduces and compares how existing standards create a profile that describes the device capabilities to achieve the goal of device independence. After acknowledging the importance of device independence, this paper utilizes the idea to introduce a Device-Aware Network (DAN). DAN provides the infrastructure support for device-content compatibility matching for data transmission. We identify the major components of the DAN architecture and issues associated with providing this new network service. A Device-Aware Network will improve the network's efficiency by preventing unusable data from consuming host and network resources. The device profile is the key issue to achieve this goal.http://archive.org/details/deviceprofilingn109451301Captain, Taiwan ArmyApproved for public release; distribution is unlimited

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