2,596 research outputs found
Continual representation learning for biometric identification
With the explosion of digital data in recent years, continuously learning new
tasks from a stream of data without forgetting previously acquired knowledge
has become increasingly important. In this paper, we propose a new continual
learning (CL) setting, namely ``continual representation learning'', which
focuses on learning better representation in a continuous way. We also provide
two large-scale multi-step benchmarks for biometric identification, where the
visual appearance of different classes are highly relevant. In contrast to
requiring the model to recognize more learned classes, we aim to learn feature
representation that can be better generalized to not only previously unseen
images but also unseen classes/identities. For the new setting, we propose a
novel approach that performs the knowledge distillation over a large number of
identities by applying the neighbourhood selection and consistency relaxation
strategies to improve scalability and flexibility of the continual learning
model. We demonstrate that existing CL methods can improve the representation
in the new setting, and our method achieves better results than the
competitors
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on
society as a whole, but also on the lives of individuals. Various prevention
measures were introduced around the world to limit the transmission of the
disease, including face masks, mandates for social distancing and regular
disinfection in public spaces, and the use of screening applications. These
developments also triggered the need for novel and improved computer vision
techniques capable of (i) providing support to the prevention measures through
an automated analysis of visual data, on the one hand, and (ii) facilitating
normal operation of existing vision-based services, such as biometric
authentication schemes, on the other. Especially important here, are computer
vision techniques that focus on the analysis of people and faces in visual data
and have been affected the most by the partial occlusions introduced by the
mandates for facial masks. Such computer vision based human analysis techniques
include face and face-mask detection approaches, face recognition techniques,
crowd counting solutions, age and expression estimation procedures, models for
detecting face-hand interactions and many others, and have seen considerable
attention over recent years. The goal of this survey is to provide an
introduction to the problems induced by COVID-19 into such research and to
present a comprehensive review of the work done in the computer vision based
human analysis field. Particular attention is paid to the impact of facial
masks on the performance of various methods and recent solutions to mitigate
this problem. Additionally, a detailed review of existing datasets useful for
the development and evaluation of methods for COVID-19 related applications is
also provided. Finally, to help advance the field further, a discussion on the
main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure
Keystroke and Touch-dynamics Based Authentication for Desktop and Mobile Devices
The most commonly used system on desktop computers is a simple username and password approach which assumes that only genuine users know their own credentials. Once broken, the system will accept every authentication trial using compromised credentials until the breach is detected. Mobile devices, such as smart phones and tablets, have seen an explosive increase for personal computing and internet browsing. While the primary mode of interaction in such devices is through their touch screen via gestures, the authentication procedures have been inherited from keyboard-based computers, e.g. a Personal Identification Number, or a gesture based password, etc.;This work provides contributions to advance two types of behavioral biometrics applicable to desktop and mobile computers: keystroke dynamics and touch dynamics. Keystroke dynamics relies upon the manner of typing rather than what is typed to authenticate users. Similarly, a continual touch based authentication that actively authenticates the user is a more natural alternative for mobile devices.;Within the keystroke dynamics domain, habituation refers to the evolution of user typing pattern over time. This work details the significant impact of habituation on user behavior. It offers empirical evidence of the significant impact on authentication systems attempting to identify a genuine user affected by habituation, and the effect of habituation on similarities between users and impostors. It also proposes a novel effective feature for the keystroke dynamics domain called event sequences. We show empirically that unlike features from traditional keystroke dynamics literature, event sequences are independent of typing speed. This provides a unique advantage in distinguishing between users when typing complex text.;With respect to touch dynamics, an immense variety of mobile devices are available for consumers, differing in size, aspect ratio, operating systems, hardware and software specifications to name a few. An effective touch based authentication system must be able to work with one user model across a spectrum of devices and user postures. This work uses a locally collected dataset to provide empirical evidence of the significant effect of posture, device size and manufacturer on user authentication performance. Based on the results of this strand of research, we suggest strategies to improve the performance of continual touch based authentication systems
Face Recognition: Issues, Methods and Alternative Applications
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition
Keystroke dynamics using auto encoders
In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user’s choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering
Incremental Learning Through Unsupervised Adaptation in Video Face Recognition
Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo]
Durante a última década, os métodos baseados en deep learning trouxeron un
salto significativo no rendemento dos sistemas de visión artificial. Unha das claves
neste éxito foi a creación de grandes conxuntos de datos perfectamente etiquetados
para usar durante o adestramento. En certa forma, as redes de deep learning
resumen esta enorme cantidade datos en prácticos vectores multidimensionais. Por
este motivo, cando as diferenzas entre os datos de adestramento e os adquiridos
durante o funcionamento dos sistemas (debido a factores como o contexto de adquisición)
son especialmente notorias, as redes de deep learning son susceptibles de
sufrir degradación no rendemento.
Mentres que a solución inmediata a este tipo de problemas sería a de recorrer a
unha recolección adicional de imaxes, co seu correspondente proceso de etiquetado,
esta dista moito de ser óptima. A gran cantidade de posibles variacións que presenta
o mundo visual converten rápido este enfoque nunha tarefa sen fin. Máis aínda cando
existen aplicacións específicas nas que esta acción é difícil, ou incluso imposible, de
realizar debido a problemas de custos ou de privacidade.
Esta tese propón abordar todos estes problemas usando a perspectiva da adaptación.
Así, a hipótese central consiste en asumir que é posible utilizar os datos non
etiquetados adquiridos durante o funcionamento para mellorar o rendemento que
obteríamos con sistemas de recoñecemento xerais. Para isto, e como proba de concepto,
o campo de estudo da tese restrinxiuse ao recoñecemento de caras. Esta é unha
aplicación paradigmática na cal o contexto de adquisición pode ser especialmente
relevante.
Este traballo comeza examinando as diferenzas intrínsecas entre algúns dos contextos
específicos nos que se pode necesitar o recoñecemento de caras e como estas
afectan ao rendemento. Desta maneira, comparamos distintas bases de datos (xunto
cos seus contextos) entre elas, usando algúns dos descritores de características máis
avanzados e así determinar a necesidade real de adaptación.
A partir desta punto, pasamos a presentar o método novo, que representa a principal
contribución da tese: o Dynamic Ensemble of SVM (De-SVM). Este método implementa
a capacidade de adaptación utilizando unha aprendizaxe incremental non
supervisada na que as súas propias predicións se usan como pseudo-etiquetas durante
as actualizacións (a estratexia de auto-adestramento). Os experimentos realizáronse
baixo condicións de vídeo-vixilancia, un exemplo paradigmático dun contexto moi
específico no que os procesos de etiquetado son particularmente complicados. As
ideas claves de De-SVM probáronse en diferentes sub-problemas de recoñecemento
de caras: a verificación de caras e recoñecemento de caras en conxunto pechado e en
conxunto aberto.
Os resultados acadados mostran un comportamento prometedor en termos de
adquisición de coñecemento sen supervisión así como robustez contra impostores.
Ademais, este rendemento é capaz de superar a outros métodos do estado da arte
que non posúen esta capacidade de adaptación.[Resumen]
Durante la última década, los métodos basados en deep learning trajeron un salto
significativo en el rendimiento de los sistemas de visión artificial. Una de las claves en
este éxito fue la creación de grandes conjuntos de datos perfectamente etiquetados
para usar durante el entrenamiento. En cierta forma, las redes de deep learning
resumen esta enorme cantidad datos en prácticos vectores multidimensionales. Por
este motivo, cuando las diferencias entre los datos de entrenamiento y los adquiridos
durante el funcionamiento de los sistemas (debido a factores como el contexto de
adquisición) son especialmente notorias, las redes de deep learning son susceptibles
de sufrir degradación en el rendimiento.
Mientras que la solución a este tipo de problemas es recurrir a una recolección
adicional de imágenes, con su correspondiente proceso de etiquetado, esta dista mucho
de ser óptima. La gran cantidad de posibles variaciones que presenta el mundo
visual convierten rápido este enfoque en una tarea sin fin. Más aún cuando existen
aplicaciones específicas en las que esta acción es difícil, o incluso imposible, de
realizar; debido a problemas de costes o de privacidad.
Esta tesis propone abordar todos estos problemas usando la perspectiva de la
adaptación. Así, la hipótesis central consiste en asumir que es posible utilizar los
datos no etiquetados adquiridos durante el funcionamiento para mejorar el rendimiento
que se obtendría con sistemas de reconocimiento generales. Para esto, y como
prueba de concepto, el campo de estudio de la tesis se restringió al reconocimiento
de caras. Esta es una aplicación paradigmática en la cual el contexto de adquisición
puede ser especialmente relevante.
Este trabajo comienza examinando las diferencias entre algunos de los contextos
específicos en los que se puede necesitar el reconocimiento de caras y así como
sus efectos en términos de rendimiento. De esta manera, comparamos distintas ba
ses de datos (y sus contextos) entre ellas, usando algunos de los descriptores de
características más avanzados para así determinar la necesidad real de adaptación.
A partir de este punto, pasamos a presentar el nuevo método, que representa la
principal contribución de la tesis: el Dynamic Ensemble of SVM (De- SVM). Este
método implementa la capacidad de adaptación utilizando un aprendizaje incremental
no supervisado en la que sus propias predicciones se usan cómo pseudo-etiquetas
durante las actualizaciones (la estrategia de auto-entrenamiento). Los experimentos
se realizaron bajo condiciones de vídeo-vigilancia, un ejemplo paradigmático de
contexto muy específico en el que los procesos de etiquetado son particularmente
complicados. Las ideas claves de De- SVM se probaron en varios sub-problemas
del reconocimiento de caras: la verificación de caras y reconocimiento de caras de
conjunto cerrado y conjunto abierto.
Los resultados muestran un comportamiento prometedor en términos de adquisición
de conocimiento así como de robustez contra impostores. Además, este rendimiento
es capaz de superar a otros métodos del estado del arte que no poseen esta
capacidad de adaptación.[Abstract]
In the last decade, deep learning has brought an unprecedented leap forward for
computer vision general classification problems. One of the keys to this success is the
availability of extensive and wealthy annotated datasets to use as training samples.
In some sense, a deep learning network summarises this enormous amount of data
into handy vector representations. For this reason, when the differences between
training datasets and the data acquired during operation (due to factors such as
the acquisition context) are highly marked, end-to-end deep learning methods are
susceptible to suffer performance degradation.
While the immediate solution to mitigate these problems is to resort to an additional
data collection and its correspondent annotation procedure, this solution
is far from optimal. The immeasurable possible variations of the visual world can
convert the collection and annotation of data into an endless task. Even more when
there are specific applications in which this additional action is difficult or simply not
possible to perform due to, among other reasons, cost-related problems or privacy
issues.
This Thesis proposes to tackle all these problems from the adaptation point of
view. Thus, the central hypothesis assumes that it is possible to use operational
data with almost no supervision to improve the performance we would achieve with
general-purpose recognition systems. To do so, and as a proof-of-concept, the field
of study of this Thesis is restricted to face recognition, a paradigmatic application
in which the context of acquisition can be especially relevant.
This work begins by examining the intrinsic differences between some of the
face recognition contexts and how they directly affect performance. To do it, we
compare different datasets, and their contexts, against each other using some of the
most advanced feature representations available to determine the actual need for
adaptation.
From this point, we move to present the novel method, representing the central
contribution of the Thesis: the Dynamic Ensembles of SVM (De-SVM). This
method implements the adaptation capabilities by performing unsupervised incremental
learning using its own predictions as pseudo-labels for the update decision
(the self-training strategy). Experiments are performed under video surveillance
conditions, a paradigmatic example of a very specific context in which labelling
processes are particularly complicated. The core ideas of De-SVM are tested in
different face recognition sub-problems: face verification and, the more complex,
general closed- and open-set face recognition.
In terms of the achieved results, experiments have shown a promising behaviour
in terms of both unsupervised knowledge acquisition and robustness against impostors,
surpassing the performances achieved by state-of-the-art non-adaptive methods.Funding and Technical Resources For the successful development of this Thesis, it was necessary to rely on series of indispensable means included in the following list:
• Working material, human and financial support primarily by the CITIC and
the Computer Architecture Group of the University of A Coruña and CiTIUS
of University of Santiago de Compostela, along with a PhD grant funded by
Xunta the Galicia and the European Social Fund.
• Access to bibliographical material through the library of the University of A
Coruña.
• Additional funding through the following research projects:
State funding by the Ministry of Economy and Competitiveness of Spain
(project TIN2017-90135-R MINECO, FEDER)
Medical System Concept of Operations for Mars Exploration Mission-11: Exploration Medical Capability (ExMC) Element - Human Research Program
NASAs exploration missions to Mars will have durations of 2-3 years and will take humans farther away from Earth than ever before. This will result in a paradigm shift for mission planning, spacecraft design, human systems integration, and in-flight medical care. Constraints on real-time communication, resupply, and medical evacuation are major architectural drivers. These constraints require medical system development to be tightly integrated with mission and vehicle design to provide crew autonomy and enable mission success. This concept of operations provides a common vision of medical care for developing a medical system for Mars exploration missions. It documents an overview of the stakeholder needs and goals of a medical system and provides examples of the types of activities the system will be used for during the mission. Development of the concept of operations considers mission variables such as distance from Earth, duration of mission, time to definitive medical care, communication protocols between crewmembers and ground support, personnel capabilities and skill sets, medical hardware and software, and medical data management. The information provided in this document informs the ExMC Systems Engineering effort to define the functions to be provided by the medical system. In addition, this concept of operations will inform the subsequent systems engineering process of developing technical requirements, system architectures, interfaces, and verification and validation approaches for the medical system. This document supports the closure of ExMC Gap Med01: We do not have a concept of operations for medical care during exploration missions, corresponding to the ExMC-managed human system risk: Risk of Adverse Health Outcomes & Decrements in Performance due to Inflight Medical Conditions. This document is applicable to the ExMC Element Systems Engineering process and may be used for collaboration within the Human Research Program
Plug-in to fear: game biosensors and negative physiological responses to music
The games industry is beginning to embark on an ambitious journey into the world of biometric gaming in search of more exciting and immersive gaming experiences. Whether or not biometric game technologies hold the key to unlock the “ultimate gaming experience” hinges not only on technological advancements alone but also on the game industry’s understanding of physiological responses to stimuli of different kinds, and its ability to interpret physiological data in terms of indicative meaning. With reference to horror genre games and music in particular, this article reviews some of the scientific literature relating to specific physiological responses induced by “fearful” or “unpleasant” musical stimuli, and considers some of the challenges facing the games industry in its quest for the ultimate “plugged-in” experience
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