5,847 research outputs found
Genetic Programming for Multibiometrics
Biometric systems suffer from some drawbacks: a biometric system can provide
in general good performances except with some individuals as its performance
depends highly on the quality of the capture. One solution to solve some of
these problems is to use multibiometrics where different biometric systems are
combined together (multiple captures of the same biometric modality, multiple
feature extraction algorithms, multiple biometric modalities...). In this
paper, we are interested in score level fusion functions application (i.e., we
use a multibiometric authentication scheme which accept or deny the claimant
for using an application). In the state of the art, the weighted sum of scores
(which is a linear classifier) and the use of an SVM (which is a non linear
classifier) provided by different biometric systems provide one of the best
performances. We present a new method based on the use of genetic programming
giving similar or better performances (depending on the complexity of the
database). We derive a score fusion function by assembling some classical
primitives functions (+, *, -, ...). We have validated the proposed method on
three significant biometric benchmark datasets from the state of the art
Verifying and Monitoring IoTs Network Behavior using MUD Profiles
IoT devices are increasingly being implicated in cyber-attacks, raising
community concern about the risks they pose to critical infrastructure,
corporations, and citizens. In order to reduce this risk, the IETF is pushing
IoT vendors to develop formal specifications of the intended purpose of their
IoT devices, in the form of a Manufacturer Usage Description (MUD), so that
their network behavior in any operating environment can be locked down and
verified rigorously. This paper aims to assist IoT manufacturers in developing
and verifying MUD profiles, while also helping adopters of these devices to
ensure they are compatible with their organizational policies and track devices
network behavior based on their MUD profile. Our first contribution is to
develop a tool that takes the traffic trace of an arbitrary IoT device as input
and automatically generates the MUD profile for it. We contribute our tool as
open source, apply it to 28 consumer IoT devices, and highlight insights and
challenges encountered in the process. Our second contribution is to apply a
formal semantic framework that not only validates a given MUD profile for
consistency, but also checks its compatibility with a given organizational
policy. We apply our framework to representative organizations and selected
devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance
testing. Finally, we show how operators can dynamically identify IoT devices
using known MUD profiles and monitor their behavioral changes on their network.Comment: 17 pages, 17 figures. arXiv admin note: text overlap with
arXiv:1804.0435
MĂ©todos discriminativos para la optimizaciĂłn de modelos en la VerificaciĂłn del Hablante
La creciente necesidad de sistemas de autenticaciĂłn seguros ha motivado el interĂ©s de algoritmos efectivos de VerificaciĂłn de Hablante (VH). Dicha necesidad de algoritmos de alto rendimiento, capaces de obtener tasas de error bajas, ha abierto varias ramas de investigaciĂłn. En este trabajo proponemos investigar, desde un punto de vista discriminativo, un conjunto de metodologĂas para mejorar el desempeño del estado del arte de los sistemas de VH. En un primer enfoque investigamos la optimizaciĂłn de los hiper-parĂĄmetros para explĂcitamente considerar el compromiso entre los errores de falsa aceptaciĂłn y falso rechazo. El objetivo de la optimizaciĂłn se puede lograr maximizando el ĂĄrea bajo la curva conocida como ROC (Receiver Operating Characteristic) por sus siglas en inglĂ©s. Creemos que esta optimizaciĂłn de los parĂĄmetros no debe de estar limitada solo a un punto de operaciĂłn y una estrategia mĂĄs robusta es optimizar los parĂĄmetros para incrementar el ĂĄrea bajo la curva, AUC (Area Under the Curve por sus siglas en inglĂ©s) de modo que todos los puntos sean maximizados. Estudiaremos cĂłmo optimizar los parĂĄmetros utilizando la representaciĂłn matemĂĄtica del ĂĄrea bajo la curva ROC basada en la estadĂstica de Wilcoxon Mann Whitney (WMW) y el cĂĄlculo adecuado empleando el algoritmo de descendente probabilĂstico generalizado. AdemĂĄs, analizamos el efecto y mejoras en mĂ©tricas como la curva detection error tradeoff (DET), el error conocido como Equal Error Rate (EER) y el valor mĂnimo de la funciĂłn de detecciĂłn de costo, minimum value of the detection cost function (minDCF) todos ellos por sue siglas en inglĂ©s. En un segundo enfoque, investigamos la señal de voz como una combinaciĂłn de atributos que contienen informaciĂłn del hablante, del canal y el ruido. Los sistemas de verificaciĂłn convencionales entrenan modelos Ășnicos genĂ©ricos para todos los casos, y manejan las variaciones de estos atributos ya sea usando anĂĄlisis de factores o no considerando esas variaciones de manera explĂcita. Proponemos una nueva metodologĂa para particionar el espacio de los datos de acuerdo a estas carcterĂsticas y entrenar modelos por separado para cada particiĂłn. Las particiones se pueden obtener de acuerdo a cada atributo. En esta investigaciĂłn mostraremos como entrenar efectivamente los modelos de manera discriminativa para maximizar la separaciĂłn entre ellos. AdemĂĄs, el diseño de algoritimos robustos a las condiciones de ruido juegan un papel clave que permite a los sistemas de VH operar en condiciones reales. Proponemos extender nuestras metodologĂas para mitigar los efectos del ruido en esas condiciones. Para nuestro primer enfoque, en una situaciĂłn donde el ruido se encuentre presente, el punto de operaciĂłn puede no ser solo un punto, o puede existir un corrimiento de forma impredecible. Mostraremos como nuestra metodologĂa de maximizaciĂłn del ĂĄrea bajo la curva ROC es mĂĄs robusta que la usada por clasificadores convencionales incluso cuando el ruido no estĂĄ explĂcitamente considerado. AdemĂĄs, podemos encontrar ruido a diferentes relaciĂłn señal a ruido (SNR) que puede degradar el desempeño del sistema. AsĂ, es factible considerar una descomposiciĂłn eficiente de las señales de voz que tome en cuenta los diferentes atributos como son SNR, el ruido y el tipo de canal. Consideramos que en lugar de abordar el problema con un modelo unificado, una descomposiciĂłn en particiones del espacio de caracterĂsticas basado en atributos especiales puede proporcionar mejores resultados. Esos atributos pueden representar diferentes canales y condiciones de ruido. Hemos analizado el potencial de estas metodologĂas que permiten mejorar el desempeño del estado del arte de los sistemas reduciendo el error, y por otra parte controlar los puntos de operaciĂłn y mitigar los efectos del ruido
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Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem
of identifying a speaker from its voice regardless of the content (i.e.
text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system.
A novel approach towards speaker identification is developed using
wavelet analysis, and multiple neural networks including Probabilistic
Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND
voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state-
of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA).
Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear.
Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identiÂŻcation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that
the proposed scheme is one of the best candidates for the fusion of
face and voice due to its low computational time and high recognition accuracy
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