3,810 research outputs found
Fast computation of the performance evaluation of biometric systems: application to multibiometric
The performance evaluation of biometric systems is a crucial step when
designing and evaluating such systems. The evaluation process uses the Equal
Error Rate (EER) metric proposed by the International Organization for
Standardization (ISO/IEC). The EER metric is a powerful metric which allows
easily comparing and evaluating biometric systems. However, the computation
time of the EER is, most of the time, very intensive. In this paper, we propose
a fast method which computes an approximated value of the EER. We illustrate
the benefit of the proposed method on two applications: the computing of non
parametric confidence intervals and the use of genetic algorithms to compute
the parameters of fusion functions. Experimental results show the superiority
of the proposed EER approximation method in term of computing time, and the
interest of its use to reduce the learning of parameters with genetic
algorithms. The proposed method opens new perspectives for the development of
secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012
Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation
We introduce caption-guided face recognition (CGFR) as a new framework to
improve the performance of commercial-off-the-shelf (COTS) face recognition
(FR) systems. In contrast to combining soft biometrics (eg., facial marks,
gender, and age) with face images, in this work, we use facial descriptions
provided by face examiners as a piece of auxiliary information. However, due to
the heterogeneity of the modalities, improving the performance by directly
fusing the textual and facial features is very challenging, as both lie in
different embedding spaces. In this paper, we propose a contextual feature
aggregation module (CFAM) that addresses this issue by effectively exploiting
the fine-grained word-region interaction and global image-caption association.
Specifically, CFAM adopts a self-attention and a cross-attention scheme for
improving the intra-modality and inter-modality relationship between the image
and textual features, respectively. Additionally, we design a textual feature
refinement module (TFRM) that refines the textual features of the pre-trained
BERT encoder by updating the contextual embeddings. This module enhances the
discriminative power of textual features with a cross-modal projection loss and
realigns the word and caption embeddings with visual features by incorporating
a visual-semantic alignment loss. We implemented the proposed CGFR framework on
two face recognition models (ArcFace and AdaFace) and evaluated its performance
on the Multi-Modal CelebA-HQ dataset. Our framework significantly improves the
performance of ArcFace in both 1:1 verification and 1:N identification
protocol.Comment: This article has been accepted for publication in the IEEE
International Joint Conference on Biometrics (IJCB), 202
Usability and Trust in Information Systems
The need for people to protect themselves and their assets is as old as humankind. People's physical safety and their possessions have always been at risk from deliberate attack or accidental damage. The advance of information technology means that many individuals, as well as corporations, have an additional range of physical (equipment) and electronic (data) assets that are at risk. Furthermore, the increased number and types of interactions in cyberspace has enabled new forms of attack on people and their possessions. Consider grooming of minors in chat-rooms, or Nigerian email cons: minors were targeted by paedophiles before the creation of chat-rooms, and Nigerian criminals sent the same letters by physical mail or fax before there was email. But the technology has decreased the cost of many types of attacks, or the degree of risk for the attackers. At the same time, cyberspace is still new to many people, which means they do not understand risks, or recognise the signs of an attack, as readily as they might in the physical world. The IT industry has developed a plethora of security mechanisms, which could be used to mitigate risks or make attacks significantly more difficult. Currently, many people are either not aware of these mechanisms, or are unable or unwilling or to use them. Security experts have taken to portraying people as "the weakest link" in their efforts to deploy effective security [e.g. Schneier, 2000]. However, recent research has revealed at least some of the problem may be that security mechanisms are hard to use, or be ineffective. The review summarises current research on the usability of security mechanisms, and discusses options for increasing their usability and effectiveness
A Survey on Soft Biometrics for Human Identification
The focus has been changed to multi-biometrics due to the security demands. The ancillary information extracted from primary biometric (face and body) traits such as facial measurements, gender, color of the skin, ethnicity, and height is called soft biometrics and can be integrated to improve the speed and overall system performance of a primary biometric system (e.g., fuse face with facial marks) or to generate human semantic interpretation description (qualitative) of a person and limit the search in the whole dataset when using gender and ethnicity (e.g., old African male with blue eyes) in a fusion framework. This chapter provides a holistic survey on soft biometrics that show major works while focusing on facial soft biometrics and discusses some of the features of extraction and classification techniques that have been proposed and show their strengths and limitations
QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios
The concerns about individuals security have justified the increasing number of surveillance
cameras deployed both in private and public spaces. However, contrary to popular belief,
these devices are in most cases used solely for recording, instead of feeding intelligent analysis
processes capable of extracting information about the observed individuals. Thus, even though
video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant
details about the subjects that took part in a crime depends on the manual inspection
of recordings. As such, the current goal of the research community is the development of
automated surveillance systems capable of monitoring and identifying subjects in surveillance
scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric
recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at
designing a visual surveillance system capable of acquiring biometric data at a distance (e.g.,
face, iris or gait) without requiring human intervention in the process, as well as devising biometric
recognition methods robust to the degradation factors resulting from the unconstrained
acquisition process.
Regarding the first goal, the analysis of the data acquired by typical surveillance systems
shows that large acquisition distances significantly decrease the resolution of biometric samples,
and thus their discriminability is not sufficient for recognition purposes. In the literature,
diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring
high-resolution imagery at a distance, particularly when using a master-slave configuration. In
the master-slave configuration, the video acquired by a typical surveillance camera is analyzed
for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged
at high-resolution by the PTZ camera. Several methods have already shown that this configuration
can be used for acquiring biometric data at a distance. Nevertheless, these methods
failed at providing effective solutions to the typical challenges of this strategy, restraining its
use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development
of a biometric data acquisition system based on the cooperation of a PTZ camera
with a typical surveillance camera. The first proposal is a camera calibration method capable
of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ
camera. The second proposal is a camera scheduling method for determining - in real-time -
the sequence of acquisitions that maximizes the number of different targets obtained, while
minimizing the cumulative transition time. In order to achieve the first goal of this thesis,
both methods were combined with state-of-the-art approaches of the human monitoring field
to develop a fully automated surveillance capable of acquiring biometric data at a distance and
without human cooperation, designated as QUIS-CAMPI system.
The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis
of the performance of the state-of-the-art biometric recognition approaches shows that these
approaches attain almost ideal recognition rates in unconstrained data. However, this performance
is incongruous with the recognition rates observed in surveillance scenarios. Taking into
account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising
biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a
distance ranging from 5 to 40 meters and without human intervention in the acquisition process.
This set allows to objectively assess the performance of state-of-the-art biometric recognition
methods in data that truly encompass the covariates of surveillance scenarios. As such, this set
was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained
by the nine methods specially designed for this competition. In addition, the data acquired by
the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the
development of methods robust to the covariates of surveillance scenarios. The first proposal
regards a method for detecting corrupted features in biometric signatures inferred by a redundancy
analysis algorithm. The second proposal is a caricature-based face recognition approach
capable of enhancing the recognition performance by automatically generating a caricature
from a 2D photo. The experimental evaluation of these methods shows that both approaches
contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivĂduos tem justificado o crescimento
do nĂșmero de cĂąmaras de vĂdeo-vigilĂąncia instaladas tanto em espaços privados como pĂșblicos.
Contudo, ao contrĂĄrio do que normalmente se pensa, estes dispositivos sĂŁo, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informaçÔes sobre os indivĂduos observados. Assim, apesar de a
vĂdeo-vigilĂąncia ter provado ser essencial na resolução de diversos crimes, o seu uso estĂĄ ainda
confinado Ă disponibilização de vĂdeos que tĂȘm que ser manualmente inspecionados para extrair
informaçÔes relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade cientĂfica Ă© o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivĂduos em ambientes de vĂdeo-vigilĂąncia.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilĂąncia. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilĂąncia que consiga adquirir dados biomĂ©tricos a longas distĂąncias
(e.g., imagens da cara, Ăris, ou vĂdeos do tipo de passo) sem requerer a cooperação dos
indivĂduos no processo; e 2) desenvolver mĂ©todos de reconhecimento biomĂ©trico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a anĂĄlise aos dados adquiridos pelos sistemas tĂpicos
de vĂdeo-vigilĂąncia mostra que, devido Ă distĂąncia de captura, os traços biomĂ©tricos amostrados
nĂŁo sĂŁo suficientemente discriminativos para garantir taxas de reconhecimento aceitĂĄveis.
Na literatura, vĂĄrios trabalhos advogam o uso de cĂąmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distùncia, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de anålise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vĂdeo adquirido por uma cĂąmara de vĂdeo-vigilĂąncia
e a cùmara PTZ é orientada para adquirir em alta resolução as regiÔes de interesse. Diversos
métodos jå mostraram que esta configuração pode ser usada para adquirir dados biométricos
Ă distĂąncia, ainda assim estes nĂŁo foram capazes de solucionar alguns problemas relacionados
com esta estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilĂąncia. Deste modo,
esta tese propÔe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilĂąncia usando uma cĂąmara PTZ assistida por uma cĂąmara tĂpica de vĂdeo-vigilĂąncia. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da cùmara
master para o Ăąngulo da cĂąmara PTZ (slave) sem o auxĂlio de outros dispositivos Ăłticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
cĂąmara PTZ. O mĂ©todo proposto consegue determinar em tempo-real a sequĂȘncia de observaçÔes
que maximiza o nĂșmero de diferentes sujeitos observados e simultaneamente minimiza o
tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os
dois métodos propostos foram combinados com os avanços alcançados na årea da monitorização
de humanos para assim desenvolver o primeiro sistema de vĂdeo-vigilĂąncia completamente automatizado
e capaz de adquirir dados biométricos a longas distùncias sem requerer a cooperação
dos indivĂduos no processo, designado por sistema QUIS-CAMPI.
O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada
com o segundo objetivo desta tese. A anålise do desempenho dos métodos de reconhecimento
biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento
quase perfeitas em dados adquiridos sem restriçÔes (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilĂąncia, o que sugere que os conjuntos
de dados atuais nĂŁo contĂȘm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilĂąncia. Tendo em conta as vulnerabilidades dos conjuntos de dados biomĂ©tricos atuais,
esta tese introduz um novo conjunto de dados biomĂ©tricos (imagens da face e vĂdeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distùncia måxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos mĂ©todos do estado-da-arte no reconhecimento de indivĂduos em imagens/vĂdeos
capturados num ambiente real de vĂdeo-vigilĂąncia. Como tal, este conjunto foi utilizado para
promover a primeira competição de reconhecimento biométrico em ambientes não controlados.
Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9
métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos
pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para
aumentar a robustez aos fatores de degradação observados em ambientes de vĂdeo-vigilĂąncia. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da anĂĄlise da redundĂąncia entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ășnica
foto do sujeito. As experiĂȘncias realizadas mostram que ambos os mĂ©todos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
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