20 research outputs found
Modeling Errors in Biometric Surveillance and De-duplication Systems
In biometrics-based surveillance and de-duplication applications, the system commonly determines if a given individual has been encountered before. In this dissertation, these applications are viewed as specific instances of a broader class of problems known as Anonymous Identification. Here, the system does not necessarily determine the identity of a person; rather, it merely establishes if the given input biometric data was encountered previously. This dissertation demonstrates that traditional biometric evaluation measures cannot adequately estimate the error rate of an anonymous identification system in general and a de-duplication system in particular. In this regard, the first contribution is the design of an error prediction model for an anonymous identification system. The model shows that the order in which individuals are encountered impacts the error rate of the system. The second contribution - in the context of an identification system in general - is an explanatory model that explains the relationship between the Receiver Operating Characteristic (ROC) curve and the Cumulative Match Characteristic (CMC) curve of a closed-set biometric system. The phenomenon of biometrics menagerie is used to explain the possibility of deducing multiple CMC curves from the same ROC curve. Consequently, it is shown that a good\u27\u27 verification system can be a poor\u27\u27 identification system and vice-versa.;Besides the aforementioned contributions, the dissertation also explores the use of gait as a biometric modality in surveillance systems operating in the thermal or shortwave infrared (SWIR) spectrum. In this regard, a new gait representation scheme known as Gait Curves is developed and evaluated on thermal and SWIR data. Finally, a clustering scheme is used to demonstrate that gait patterns can be clustered into multiple categories; further, specific physical traits related to gender and body area are observed to impact cluster generation.;In sum, the dissertation provides some new insights into modeling anonymous identification systems and gait patterns for biometrics-based surveillance systems
Towards Engineering Reliable Keystroke Biometrics Systems
In this thesis, we argue that most of the work in the literature on behavioural-based biometric systems using AI and machine learning is immature and unreliable. Our analysis and experimental results show that designing reliable behavioural-based biometric systems requires a systematic and complicated process. We first discuss the limitation in existing work and the use of conventional machine learning methods. We use the biometric zoos theory to demonstrate the challenge of designing reliable behavioural-based biometric systems. Then, we outline the common problems in engineering reliable biometric systems. In particular, we focus on the need for novelty detection machine learning models and adaptive machine learning algorithms. We provide a systematic approach to design and build reliable behavioural-based biometric systems. In our study, we apply the proposed approach to keystroke dynamics. Keystroke dynamics is behavioural-based biometric that identify individuals by measuring their unique typing behaviours on physical or soft keyboards. Our study shows that it is possible to design reliable behavioral-based biometrics and address the gaps in the literature
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation
Latent fingerprint identification is attracting increasing interest because of its important role
in law enforcement. Although the use of various fingerprint features might be required for successful latent
fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform
other methods. However, as many fingerprint feature representations exist, we sought to determine if the
selection of feature representation has an impact on the performance of automated fingerprint identification
systems. In this paper, we review the most prominent fingerprint feature representations reported in the
literature, identify trends in fingerprint feature representation, and observe that representations designed for
verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of
the most popular fingerprint feature representations over a common latent fingerprint database. Therefore,
we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification
in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our
experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than
10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating
the need of new minutia descriptors for latent fingerprint identification.This work was supported in part by the National Council of Science and Technology of Mexico (CONACYT) under Grant PN-720 and
Grant 63894
Gait Recognition: Databases, Representations, and Applications
There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition
Proof-of-Concept
Biometry is an area in great expansion and is considered as possible solution to cases where high
authentication parameters are required. Although this area is quite advanced in theoretical
terms, using it in practical terms still carries some problems. The systems available still depend
on a high cooperation level to achieve acceptable performance levels, which was the backdrop
to the development of the following project. By studying the state of the art, we propose the
creation of a new and less cooperative biometric system that reaches acceptable performance
levels.A constante necessidade de parâmetros mais elevados de segurança, nomeadamente ao nível
de autenticação, leva ao estudo biometria como possível solução. Actualmente os mecanismos
existentes nesta área tem por base o conhecimento de algo que se sabe ”password” ou algo
que se possui ”codigo Pin”. Contudo este tipo de informação é facilmente corrompida ou contornada.
Desta forma a biometria é vista como uma solução mais robusta, pois garante que a
autenticação seja feita com base em medidas físicas ou compartimentais que definem algo que
a pessoa é ou faz (”who you are” ou ”what you do”).
Sendo a biometria uma solução bastante promissora na autenticação de indivíduos, é cada vez
mais comum o aparecimento de novos sistemas biométricos. Estes sistemas recorrem a medidas
físicas ou comportamentais, de forma a possibilitar uma autenticação (reconhecimento) com
um grau de certeza bastante considerável. O reconhecimento com base no movimento do corpo
humano (gait), feições da face ou padrões estruturais da íris, são alguns exemplos de fontes
de informação em que os sistemas actuais se podem basear. Contudo, e apesar de provarem
um bom desempenho no papel de agentes de reconhecimento autónomo, ainda estão muito
dependentes a nível de cooperação exigida. Tendo isto em conta, e tudo o que já existe no
ramo do reconhecimento biometrico, esta área está a dar passos no sentido de tornar os seus
métodos o menos cooperativos poss??veis. Possibilitando deste modo alargar os seus objectivos
para além da mera autenticação em ambientes controlados, para casos de vigilância e controlo
em ambientes não cooperativos (e.g. motins, assaltos, aeroportos).
É nesta perspectiva que o seguinte projecto surge. Através do estudo do estado da arte, pretende
provar que é possível criar um sistema capaz de agir perante ambientes menos cooperativos,
sendo capaz de detectar e reconhecer uma pessoa que se apresente ao seu alcance.O
sistema proposto PAIRS (Periocular and Iris Recognition Systema) tal como nome indica, efectua
o reconhecimento através de informação extraída da íris e da região periocular (região circundante
aos olhos). O sistema é construído com base em quatro etapas: captura de dados,
pré-processamento, extração de características e reconhecimento. Na etapa de captura de
dados, foi montado um dispositivo de aquisição de imagens com alta resolução com a capacidade
de capturar no espectro NIR (Near-Infra-Red). A captura de imagens neste espectro tem
como principal linha de conta, o favorecimento do reconhecimento através da íris, visto que
a captura de imagens sobre o espectro visível seria mais sensível a variações da luz ambiente.
Posteriormente a etapa de pré-processamento implementada, incorpora todos os módulos do
sistema responsáveis pela detecção do utilizador, avaliação de qualidade de imagem e segmentação
da íris. O modulo de detecção é responsável pelo desencadear de todo o processo, uma
vez que esta é responsável pela verificação da exist?ncia de um pessoa em cena. Verificada
a sua exist?ncia, são localizadas as regiões de interesse correspondentes ? íris e ao periocular,
sendo também verificada a qualidade com que estas foram adquiridas. Concluídas estas
etapas, a íris do olho esquerdo é segmentada e normalizada. Posteriormente e com base em
vários descritores, é extraída a informação biométrica das regiões de interesse encontradas,
e é criado um vector de características biométricas. Por fim, é efectuada a comparação dos
dados biometricos recolhidos, com os já armazenados na base de dados, possibilitando a criação
de uma lista com os níveis de semelhança em termos biometricos, obtendo assim um resposta
final do sistema. Concluída a implementação do sistema, foi adquirido um conjunto de imagens capturadas através do sistema implementado, com a participação de um grupo de voluntários.
Este conjunto de imagens permitiu efectuar alguns testes de desempenho, verificar e afinar
alguns parâmetros, e proceder a optimização das componentes de extração de características e
reconhecimento do sistema. Analisados os resultados foi possível provar que o sistema proposto
tem a capacidade de exercer as suas funções perante condições menos cooperativas
Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods
Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains
Model-based 3d gait biometric using quadruple fusion classifier
The area of gait biometrics has received significant interest in the last few years, largely due to the unique suitability and reliability of gait pattern as a human recognition technique. The advantage of gait over other biometrics is that it can perform non-intrusive data acquisition and can be captured from a distance. Current gait analysis approach can be divided into model-free and model-based approach. The gait data extracted for identification process may be influenced by ambient noise conditions, occlusion, changes in backgrounds and illumination when model-free 2D silhouette data is considered. In addition, the performance in gait biometric recognition is often affected by covariate factors such as walking condition and footwear. These are often related to low performance of personal verification and identification. While body biometrics constituted of both static and dynamics features of gait motion, they can complement one another when used jointly to maximise recognition performance. Therefore, this research proposes a model-based technique that can overcome the above limitations. The proposed technique covers the process of extracting a set of 3D static and dynamic gait features from the 3D skeleton data in different covariate factors such as different footwear and walking condition. A skeleton model from forty subjects was acquired using Kinect which was able to provide human skeleton and 3D joints and the features were extracted and categorized into static and dynamic data. Normalization process was performed to scale down the features into a specific range of structure, followed by feature selection process to select the most significant features to be used in classification. The features were classified separately using five classification algorithms for static and dynamic features. A new fusion framework is proposed based on score level fusion called Quadruple Fusion Framework (QFF) in order to combine the static and dynamic features obtained from the classification model. The experimental result of static and dynamic fusion achieved the accuracy of 99.5% for footwear covariates and 97% for walking condition covariates. The result of the experimental validation demonstrated the viability of gait as biometrics in human recognition
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
Soft-biometrics play an important role in face biometrics and related fields
since these might lead to biased performances, threatens the user's privacy, or
are valuable for commercial aspects. Current face databases are specifically
constructed for the development of face recognition applications. Consequently,
these databases contain large amount of face images but lack in the number of
attribute annotations and the overall annotation correctness. In this work, we
propose MAADFace, a new face annotations database that is characterized by the
large number of its high-quality attribute annotations. MAADFace is build on
the VGGFace2 database and thus, consists of 3.3M faces of over 9k individuals.
Using a novel annotation transfer-pipeline that allows an accurate
label-transfer from multiple source-datasets to a target-dataset, MAAD-Face
consists of 123.9M attribute annotations of 47 different binary attributes.
Consequently, it provides 15 and 137 times more attribute labels than CelebA
and LFW. Our investigation on the annotation quality by three human evaluators
demonstrated the superiority of the MAAD-Face annotations over existing
databases. Additionally, we make use of the large amount of high-quality
annotations from MAAD-Face to study the viability of soft-biometrics for
recognition, providing insights about which attributes support genuine and
imposter decisions. The MAAD-Face annotations dataset is publicly available.Comment: Accepted in IEEE Transactions on Information Forensics and Securit