4,060 research outputs found
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
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 indivíduos tem justificado o crescimento
do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos.
Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a
vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivíduos em ambientes de vídeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias
(e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos
indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos
de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados
não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis.
Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância
e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos
métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos
à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados
com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara
master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações
que maximiza o número 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 área da monitorização
de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivíduos 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 análise 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 restrições (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos
de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de
vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais,
esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos
capturados num ambiente real de vídeo-vigilância. 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 vídeo-vigilância. O
primeiro é um método para detetar características corruptas em assinaturas biométricas através
da análise da redundância entre subconjuntos de características. O segundo é um método de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma não controlada
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
© 1979-2012 IEEE. To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract 'Multi-Directional Multi-Level Dual-Cross Patterns' (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
An efficient multiscale scheme using local zernike moments for face recognition
In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.WOS:000437326800174Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ2 - Q3ArticleUluslararası işbirliği ile yapılmayan - HAYIRMayıs2018YÖK - 2017-1
Valvekaameratel põhineva inimseire täiustamine pildi resolutsiooni parandamise ning näotuvastuse abil
Due to importance of security in the society, monitoring activities and recognizing specific
people through surveillance video camera is playing an important role. One of
the main issues in such activity rises from the fact that cameras do not meet the resolution
requirement for many face recognition algorithms. In order to solve this issue,
in this work we are proposing a new system which super resolve the image. First,
we are using sparse representation with the specific dictionary involving many natural
and facial images to super resolve images. As a second method, we are using deep
learning convulutional network. Image super resolution is followed by Hidden Markov
Model and Singular Value Decomposition based face recognition. The proposed system
has been tested on many well-known face databases such as FERET, HeadPose, and
Essex University databases as well as our recently introduced iCV Face Recognition
database (iCV-F). The experimental results shows that the recognition rate is increasing
considerably after applying the super resolution by using facial and natural image
dictionary. In addition, we are also proposing a system for analysing people movement
on surveillance video. People including faces are detected by using Histogram of Oriented
Gradient features and Viola-jones algorithm. Multi-target tracking system with
discrete-continuouos energy minimization tracking system is then used to track people.
The tracking data is then in turn used to get information about visited and passed
locations and face recognition results for tracked people
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