111 research outputs found
Cardiovascular information for improving biometric recognition
Mención Internacional en el tÃtulo de doctorThe improvements of the last two decades in data modeling and computing have lead
to new biometric modalities. The Electrocardiogram (ECG) modality is part of them, and
has been mainly researched by using public databases related to medical training. Despite
of being useful for initial approaches, they are not representative of a real biometric
environment. In addition, publishing and creating a new database is none trivial due
to human resources and data protection laws.
The main goal of this thesis is to successfully use ECG as a biometric signal while
getting closer to the real case scenario. Every experiment considers low computational
calculations and transformations to help in potential portability. The core experiments
in this work come from a private database with different positions, time and heart rate
scenarios. An initial segmentation evaluation is achieved with the help of fiducial point
detection which determines the QRS selection as the input data for all the experiments.
The approach of training a model per user (open-set) is tested with different machine
learning algorithms, only getting an acceptable result with Gaussian Mixture Models
(GMM). However, the concept of training all users in one model (closed-set) shows
more potential with Linear Discriminant Analysis (LDA), whose results were improved
in 40%. The results with LDA are also tested as a multi-modality technique, decreasing
the Equal Error Rate (EER) of fingerprint verification in up to 70.64% with score fusion,
and reaching 0% in Protection Attack Detection (PAD).
The Multilayer Perceptron (MLP) algorithm enhances these results in verification
while applying the first differentiation to the signal. The network optimization is achieved
with EER as an observation metric, and improves the results of LDA in 22% for the worst
case scenario, and decreases the EER to 0% in the best case. Complexity is added creating
a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) based
network, BioECG. The tuning process is achieved without extra feature transformation
and is evaluated through accuracy, aiming for good identification. The inclusion of a
second day of enrollment in improves results from MLP, reaching the overall lowest
results of 0.009%–1.352% in EER.
Throughout the use of good quality signals, position changes did not noticeably impact
the verification. In addition, collecting data in a different day or in a different hour did
not clearly affect the performance. Moreover, modifying the verification process based on
attempts, improves the overall results, up to reach a 0% EER when applying BioECG.
Finally, to get closer to a real scenario, a smartband prototype is used to collect new
databases. A private database with limited scenarios but controlled data, and another
local database with a wider range of scenarios and days, and with a more relaxed use of
the device. Applying the concepts of first differentiation and MLP, these signals required the Stationary Wavelet Transform (SWT) and new fiducial point detection to improve
their results. The first database gave subtle chances of being used in identification with
up to 78.2% accuracy, but the latter was completely discarded for this purpose. These
realistic experiments show the impact of a low fidelity sensor, even considering the same
modifications in previous successful experiments with better quality data, reaching up to
13.530% EER. In the second database, results reach a range of 0.068%–31.669% EER.
This type of sensor is affected by heart rate changes, but also by position variations, given
its sensitivity to movement.Las mejoras en modelado de datos y computación de las últimas dos décadas,
han llevado a la creación de nuevas modalidades biométricas. La modalidad de
electrocardiograma (ECG) es una de ellas, la cual se ha investigado usando bases de datos
públicas que fueron creadas para entrenamiento de profesional médico. Aunque estos
datos han sido útiles para los estados iniciales de la modalidad, no son representativos de
un entorno biométrico real. Además, publicar y crear bases de datos nuevas son problemas
no triviales debido a los recursos humanos y las leyes de protección de datos.
El principal objetivo de esta tesis es usar exitosamente datos de ECG como señales
biométricas a la vez que nos acercamos a un escenario realista. Cada experimento
considera cálculos y transformadas de bajo coste computacional para ayudar en su
potencial uso en aparatos móviles. Los principales experimentos de este trabajo se
producen con una base de datos privada con diferentes escenarios en términos de postura,
tiempo y frecuencia cardÃaca. Con ella se evalúan las diferentes seleccións del complejo
QRS mediante detección de puntos fiduciales, lo cual servirá como datos de entrada para
el resto de experimentos.
El enfoque de entrenar un modelo por usuario (open-set) se prueba con diferentes
algoritmos de aprendizaje máquina (machine learning), obteniendo resultados aceptables
únicamente mediante el uso de modelos de mezcla de Gaussianas (Gaussian Mixture
Models, GMM). Sin embargo, el concepto de entrenar un modelo con todos los usuarios
(closed-set) demuestra mayor potencial con Linear Discriminant Analysis (Análisis de
Discriminante Lineal, LDA), cuyos resultados mejoran en un 40%. Los resultados de
LDA también se utilizan como técnica multi-modal, disminuyendo la Equal Error Rate
(Tasa de Igual Error, EER) de la verificación mediante huella en hasta un 70.64% con
fusión de puntuación, y llegando a un sistema con un 0% de EER en Detección de Ataques
de Presentación (Presentation Attack Detection, PAD).
El algoritmo de Perceptrón Multicapa (Multilayer Perceptron, MLP) mejora los
resultados previos en verificación aplicando la primera derivada a la señal. La
optimización de la red se consigue en base a su EER, mejora la de LDA en hasta un 22%
en el peor caso, y la lleva hasta un 0% en el mejor caso. Se añade complejidad creando una
red neural convolucional (Convolutional Neural Network, CNN) con una red de memoria
a largo-corto plazo (Long-Short Term Memory, LSTM), llamada BioECG. El proceso de
ajuste de hiperparámetros se lleva acabo sin transformaciones y se evalúa observando la
accuracy (precisión), para mejorar la identificación. Sin embargo, incluir un segundo dÃa
de registro (enrollment) con BioECG, estos resultados mejoran hasta un 74% para el peor
caso, llegando a los resultados más bajos hasta el momento con 0.009%–1.352% en la
EER.
Durante el uso de señales de buena calidad, los cambios de postura no afectaron notablemente a la verificación. Además, adquirir los datos en dÃas u horas diferentes
tampoco afectó claramente a los resultados. Asimismo, modificar el proceso de
verificación en base a intentos también produce mejorÃa en todos los resultados, hasta
el punto de llegar a un 0% de EER cuando se aplica BioECG.
Finalmente, para acercarnos al caso más realista, se usa un prototipo de pulsera para
capturar nuevas bases de datos. Una base de datos privada con escenarios limitados pero
datos más controlados, y otra base de datos local con más espectro de escenarios y dÃas y
un uso del dispositivo más relajado. Para estos datos se aplican los conceptos de primera
diferenciación en MLP, cuyas señales requieren la Transformada de Wavelet Estacionaria
(Stationary Wavelet Transform, SWT) y un detector de puntos fiduciales para mejorar los
resultados. La primera base de datos da opciones a ser usada para identificación con un
máximo de precisión del 78.2%, pero la segunda se descartó completamente para este
propósito. Estos experimentos más realistas demuestran el impact de tener un sensor de
baja fidelidad, incluso considerando las mismas modificaciones que previamente tuvieron
buenos resultados en datos mejores, llegando a un 13.530% de EER. En la segunda base
de datos, los resultados llegan a un rango de 0.068%–31.669% en EER. Este tipo de sensor
se ve afectado por las variaciones de frecuencia cardÃaca, pero también por el cambio de
posición, dado que es más sensible al movimiento.Programa de Doctorado en IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Cristina Conde Vilda.- Secretario: Mariano López GarcÃa.- Vocal: Young-Bin Know
Threshold-optimized decision-level fusion and its application to biometrics
Fusion is a popular practice to increase the reliability of biometric verification. In this paper, we propose an optimal fusion scheme at decision level by the AND or OR rule, based on optimizing matching score thresholds. The proposed fusion scheme will always give an improvement in the Neyman–Pearson sense over the component classifiers that are fused. The theory of the threshold-optimized decision-level fusion is presented, and the applications are discussed. Fusion experiments are done on the FRGC database which contains 2D texture data and 3D shape data. The proposed decision fusion improves the system performance, in a way comparable to or better than the conventional score-level fusion. It is noteworthy that in practice, the threshold-optimized decision-level fusion by the OR rule is especially useful in presence of outliers
Análise de propriedades intrÃnsecas e extrÃnsecas 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 legÃtimo, 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 vÃdeo digital ou uma máscara 3D com as informações faciais de um usuário-alvo. Em sistemas de biometria baseados em Ãris, os ataques de apresentação podem ser realizados com fotografias impressas ou com lentes de contato contendo os padrões de Ãris 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 construÃdas 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 Ãris 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 ruÃdo; em propriedades intrÃnsecas das amostras biométricas (e.g., mapas de albedo, de reflectância e de profundidade) e em técnicas de aprendizagem supervisionada de caracterÃsticas. Os principais resultados e contribuições apresentadas nesta tese incluem: a criação de um grande conjunto de dados publicamente disponÃvel contendo aproximadamente 17K videos de simulações de ataques de apresentações e de acessos genuÃnos 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 extrÃnsecas 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 intrÃnsecas das faces, estimadas a partir da informação de sombras presentes em sua superfÃcie; e, por fim, a investigação de diferentes abordagens baseadas em redes neurais convolucionais para o aprendizado automático de caracterÃsticas 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 caracterÃsticas relacionadas ao nosso problema a partir de pequenos conjuntos de dados disponÃveis 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
Graph-Based Offline Signature Verification
Graphs provide a powerful representation formalism that offers great promise
to benefit tasks like handwritten signature verification. While most
state-of-the-art approaches to signature verification rely on fixed-size
representations, graphs are flexible in size and allow modeling local features
as well as the global structure of the handwriting. In this article, we present
two recent graph-based approaches to offline signature verification: keypoint
graphs with approximated graph edit distance and inkball models. We provide a
comprehensive description of the methods, propose improvements both in terms of
computational time and accuracy, and report experimental results for four
benchmark datasets. The proposed methods achieve top results for several
benchmarks, highlighting the potential of graph-based signature verification
The Use of EEG Signals For Biometric Person Recognition
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
Biometric layering: template security and privacy through multi-biometric template fusion
As biometric applications are gaining popularity, there is increased concern over the loss of privacy and potential misuse of biometric data held in central repositories. Biometric template protection mechanisms suggested in recent years aim to address these issues by securing the biometric data in a template or other structure such that it is suitable for authentication purposes, while being protected against unauthorized access or crosslinking attacks. We propose a biometric authentication framework for enhancing privacy and template security, by layering multiple biometric modalities to construct a multi-biometric template such that it is difficult to extract or separate the individual layers. Thus, the framework uses the subject's own biometric to conceal her biometric data, while it also enjoys the performance benefits because of the use of multiple modalities. The resulting biometric template is also cancelable if the system is implemented with cancelable biometrics such as voice. We present two different realizations of this idea: one combining two different fingerprints and another one combining a fingerprint and a spoken passphrase. In either case, both biometric samples are required for successful authentication, leading to increased security, in addition to privacy gains. The performance of the proposed framework is evaluated using the FVC 2000-2002 and NIST fingerprint databases, and the TUBITAK MTRD speaker database. Results show only a small degradation in EER compared to a state-of-the-art ngerprint verification system and high identification rates, while cross-link rates are low even with very small databases
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users
We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones)
- …