389 research outputs found
Towards Better Image Embeddings Using Neural Networks
The primary focus of this dissertation is to study image embeddings extracted by neural networks. Deep Learning (DL) is preferred over traditional Machine Learning (ML) for the reason that feature representations can be automatically constructed from data without human involvement. On account of the effectiveness of deep features, the last decade has witnessed unprecedented advances in Computer Vision (CV), and more real-world applications are expected to be introduced in the coming years.
A diverse collection of studies has been included, covering areas such as person re-identification, vehicle attribute recognition, neural image compression, clustering and unsupervised anomaly detection. More specifically, three aspects of feature representations have been thoroughly analyzed. Firstly, features should be distinctive, i.e., features of samples from distinct categories ought to differ significantly. Extracting distinctive features is essential for image retrieval systems, in which an algorithm finds the gallery sample that is closest to a query sample. Secondly, features should be privacy-preserving, i.e., inferring sensitive information from features must be infeasible. With the widespread adoption of Machine Learning as a Service (MLaaS), utilizing privacy-preserving features prevents privacy violations even if the server has been compromised. Thirdly, features should be compressible, i.e., compact features are preferable as they require less storage space. Obtaining compressible features plays a vital role in data compression.
Towards the goal of deriving distinctive, privacy-preserving and compressible feature representations, research articles included in this dissertation reveal different approaches to improving image embeddings learned by neural networks. This topic remains a fundamental challenge in Machine Learning, and further research is needed to gain a deeper understanding
Affect recognition & generation in-the-wild
Affect recognition based on a subject’s facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This Ph.D. Thesis exploits these advances and makes significant contributions for affect analysis and recognition in-the-wild.
We tackle affect analysis and recognition as a dual knowledge generation problem: i) we create new, large and rich in-the-wild databases and ii) we design and train novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets.
At first, we present the creation of Aff-Wild database annotated according to valence-arousal and an end-to-end CNN-RNN architecture, AffWildNet. Then we use AffWildNet as a robust prior for dimensional and categorical affect recognition and extend it by extracting low-/mid-/high-level latent information and analysing this via multiple RNNs. Additionally, we propose a novel loss function for DNN-based categorical affect recognition.
Next, we generate Aff-Wild2, the first database containing annotations for all main behavior tasks: estimate Valence-Arousal; classify into Basic Expressions; detect Action Units. We develop multi-task and multi-modal extensions of AffWildNet by fusing these tasks and propose a novel holistic approach that utilises all existing databases with non-overlapping annotations and couples them through co-annotation and distribution matching.
Finally, we present an approach for valence-arousal, or basic expressions’ facial affect synthesis. We generate an image with a given affect, or a sequence of images with evolving affect, by annotating a 4-D database and utilising a 3-D morphable model.Open Acces
Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016
These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions
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 nĂşmero de câmaras de vĂdeo-vigilância instaladas tanto em espaços privados como pĂşblicos.
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 indivĂduos observados. Assim, apesar de a
vĂdeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado Ă disponibilização de vĂdeos que tĂŞm que ser manualmente inspecionados para extrair
informações 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-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilância que consiga adquirir dados biomĂ©tricos a longas distâncias
(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 análise aos dados adquiridos pelos sistemas tĂpicos
de vĂdeo-vigilância mostra que, devido Ă distância de captura, os traços biomĂ©tricos 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 vĂdeo adquirido por uma câmara de vĂdeo-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 estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilância usando uma câmara PTZ assistida por uma câmara tĂpica de vĂdeo-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 auxĂlio de outros dispositivos Ăłticos. 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 nĂşmero 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 vĂdeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias 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 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 nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilância, o que sugere que os conjuntos
de dados atuais nĂŁo contĂŞm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilância. 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 distância máxima 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-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 vĂdeo-vigilância. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da análise da redundância entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ăşnica
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
Activity related biometrics for person authentication
One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods.
The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics.
Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features.
Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space.
Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements.
The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance.
The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces
Enhancing open-set face recognition by closing it with Cluster-Inferred Gallery Augmentation
In open-set face recognition - as opposed to closed-set face recognition - it is possible that the identity of a given query is not present in the gallery set. In that case, the identity of the query can only be correctly classified as "unknown" when the similarity with the gallery faces is below a threshold that was determined a priori. However, in many use-cases, the set of queries contains multiple instances of the same identity, whether or not this identity is represented in the gallery. Thus, the set of query faces lends itself to identity clustering that could yield representative instances for unknown identities. By augmenting the gallery with these instances, we can make an open-set face recognition problem more closed. In this paper, we show that this method of Cluster-Inferred Gallery Augmentation (CIGA) does indeed improve the quality of open-set face recognition. We evaluate the addition of CIGA for both a private dataset of images taken in a school context and the public LFW dataset, showing a significant improvement in both cases. Moreover, an implementation of the suggested approach along with our experiments are made publicly available on https://gitlab.com/florisdf/acpr2019.status: accepte
Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity
The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method
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