Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition

Iris recognition algorithms, especially with the emergence of large-scale iris-based identification systems, must be tested for speed and accuracy and evaluated with a wide range of templates – large size, long-range, visible and different origins. This paper presents the acquisition of eye-iris images of dark-skinned subjects in Africa, a predominant case of verydark- brown iris images, under near-infrared illumination. The peculiarity of these iris images is highlighted from the histogram and normal probability distribution of their grayscale image entropy (GiE) values, in comparison to Asian and Caucasian iris images. The acquisition of eye-images for the African iris dataset is ongoing and will be made publiclyavailable as soon as it is sufficiently populated

Biometrics in forensic science: challenges, lessons and new technologies

Biometrics has historically found its natural mate in Forensics. The first applications found in the literature and over cited so many times, are related to biometric measurements for the identification of multiple offenders from some of their biometric and anthropometric characteristics (tenprint cards) and individualization of offender from traces found on crime-scenes (e.g. fingermarks, earmarks, bitemarks, DNA). From sir Francis Galton, to the introduction of AFIS systems in the scientific laboratories of police departments, Biometrics and Forensics have been "dating" with alternate results and outcomes. As a matter of facts there are many technologies developed under the "Biometrics umbrella" which may be optimised to better impact several Forensic scenarios and criminal investigations. At the same time, there is an almost endless list of open problems and processes in Forensics which may benefit from the introduction of tailored Biometric technologies. Joining the two disciplines, on a proper scientific ground, may only result in the success for both fields, as well as a tangible benefit for the society. A number of Forensic processes may involve Biometric-related technologies, among them: Evidence evaluation, Forensic investigation, Forensic Intelligence, Surveillance, Forensic ID management and Verification.\ud The COST Action IC1106 funded by the European Commission, is trying to better understand how Biometric and Forensics synergies can be exploited within a pan-European scientific alliance which extends its scope to partners from USA, China and Australia.\ud Several results have been already accomplished pursuing research in this direction. Notably the studies in 2D and 3D face recognition have been gradually applied to the forensic investigation process. In this paper a few solutions will be presented to match 3D face shapes along with some experimental results

Efficient Human Activity Recognition in Large Image and Video Databases

Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images

Modelação computacional do membro inferior humano para a reprodução da dinâmica da marcha com músculos: casos saudável e patológico

Dissertação de mestrado integrado em Engenharia BiomédicaA biomecânica do movimento compreende o estudo e análise do movimento realizado por seres vivos, quer seja para melhorar o seu desempenho ou prevenir e tratar lesões. O primeiro é amplamente aplicado ao estudo do desporto e ao auxílio prestado aos atletas para desempenhar um movimento pretendido. De modo a ajudar na prevenção e tratamento de lesões, a biomecânica visa providenciar conhecimento acerca das propriedades mecânicas dos tecidos humanos, das cargas mecânicas a que eles estão sujeitos durante o movimento e de terapias relacionadas com prevenção e reabilitação. Atua no sentido de auxiliar na projeção estratégias de reabilitação e dispositivos médicos de assistência. Este trabalho compreende o uso de um modelo biomecânico do membro inferior humano previamente desenvolvido por Geyer & Herr (2010). O modelo inclui a reprodução dos sistemas esquelético, muscular e neuronal humanos com o objetivo de produzir o movimento do modelo. Esta abordagem foi utilizada para estudar eventos que ocorrem a nível articular, considerando, por exemplo, a amplitude de movimento e os torques produzidos, durante uma marcha fisiológica e patológica com lesão do ligamento anterior cruzado (LAC). Esta patologia está associada a atividades, tais como o basquetebol, envolvendo mudanças rápidas na direção combinadas com aceleração e desaceleração do corpo. Estas ações causam lesões do LAC e por vezes levam à sua rutura, provocando a necessidade de procedimentos de reabilitação e cirúrgicos para melhorar a qualidade de vida dos pacientes. Para o modelo biomecânico saudável, considerando os ângulos das juntas, os resultados apresentam uma concordância muito boa com a literatura. Os dados cinéticos demonstram algumas semelhanças, bem como discrepâncias. O torque da articulação do tornozelo é o que mais se aproxima da literatura, enquanto que o torque da anca e do joelho apresentam diferenças tanto em magnitude (maior do que o esperado), como em forma. Os padrões de ativação muscular também apresentam diferenças quando comparados com a literatura. Sugestões são dadas para minimizar estas ocorrências. A implementação da patologia consistiu na diminuição do torque produzido pelos músculos (quadríceps e hamstrings) afetados pela lesão do ligamento anterior cruzado após reconstrução cirúrgica. A variável mais próxima da literatura é a força vertical de reação com o solo, enquanto que os restantes dados cinéticos diferem. Sugestões são dadas de forma a equivaler mais os resultados com a literatura.Biomechanics of movement comprises the study and analysis of the movement performed by living beings, whether by improving its performance or by preventing and treating injury. The former is extensively applied to understanding sports and to help athletes performing a desired movement. In order to help preventing and treating injury, biomechanics aims at providing knowledge on the mechanical properties of human tissues, the mechanical loads they feel during movement, and on therapies related to prevention and rehabilitation. It acts to help design rehabilitation procedures and assistive medical devices. This work comprises the use of a biomechanical model of the human lower limb previously developed by Geyer & Herr (2010). The model possesses the reproduction of the human skeletal, muscular and neural systems in order to produce the model’s movement. This approach was used to study the events occurring at joint level, regarding, for instance, its range of motion and produced torques, during physiological and anterior cruciate ligament (ACL) pathological gait. This gait pathology is associated with activities, such as basketball, involving rapid changes in direction combined with acceleration and deceleration of the body. These actions cause ACL injuries and sometimes lead to its rupture, provoking the need for surgical and rehabilitation procedures to improve the patient’s life quality. For the healthy biomechanical model, regarding the joints’ angles, the results present very good agreement with results found in literature. The kinetic data show some similarities, as well as discrepancies. The ankle joint torque is the closest to literature findings, whilst the hip and knee joint torque present differences both in magnitude (higher than expected) and in shape. The muscular activation patterns also present differences when compared to literature. Suggestions are made in order to minimize these occurrences. The implementation of pathology consisted in diminishing the torque produced by the muscles (quadriceps and hamstrings) affected by an anterior cruciate ligament injury after its surgical reconstruction. The variable closer to literature findings is the vertical ground reaction force, whilst the other kinetic and kinematic data differ. Suggestions are made in order to make the results more equivalent to literature

Ergonomic Models of Anthropometry, Human Biomechanics and Operator-Equipment Interfaces

The Committee on Human Factors was established in October 1980 by the Commission on Behavioral and Social Sciences and Education of the National Research Council. The committee is sponsored by the Office of Naval Research, the Air Force Office of Scientific Research, the Army Research Institute for the Behavioral and Social Sciences, the National Aeronautics and Space Administration, and the National Science Foundation. The workshop discussed the following: anthropometric models; biomechanical models; human-machine interface models; and research recommendations. A 17-page bibliography is included

Progress in ambient assisted systems for independent living by the elderly

One of the challenges of the ageing population in many countries is the efficient delivery of health and care services, which is further complicated by the increase in neurological conditions among the elderly due to rising life expectancy. Personal care of the elderly is of concern to their relatives, in case they are alone in their homes and unforeseen circumstances occur, affecting their wellbeing. The alternative; i.e. care in nursing homes or hospitals is costly and increases further if specialized care is mobilized to patients’ place of residence. Enabling technologies for independent living by the elderly such as the ambient assisted living systems (AALS) are seen as essential to enhancing care in a cost-effective manner. In light of significant advances in telecommunication, computing and sensor miniaturization, as well as the ubiquity of mobile and connected devices embodying the concept of the Internet of Things (IoT), end-to-end solutions for ambient assisted living have become a reality. The premise of such applications is the continuous and most often real-time monitoring of the environment and occupant behavior using an event-driven intelligent system, thereby providing a facility for monitoring and assessment, and triggering assistance as and when needed. As a growing area of research, it is essential to investigate the approaches for developing AALS in literature to identify current practices and directions for future research. This paper is, therefore, aimed at a comprehensive and critical review of the frameworks and sensor systems used in various ambient assisted living systems, as well as their objectives and relationships with care and clinical systems. Findings from our work suggest that most frameworks focused on activity monitoring for assessing immediate risks while the opportunities for integrating environmental factors for analytics and decision-making, in particular for the long-term care were often overlooked. The potential for wearable devices and sensors, as well as distributed storage and access (e.g. cloud) are yet to be fully appreciated. There is a distinct lack of strong supporting clinical evidence from the implemented technologies. Socio-cultural aspects such as divergence among groups, acceptability and usability of AALS were also overlooked. Future systems need to look into the issues of privacy and cyber security

Orthopaedic sport biomechanics: a new paradigm

This article proposes a new paradigm, "Orthopaedic sport biomechanics", for the understanding of the role of biomechanics in preventing and managing sports injury. Biomechanics has three main roles in this paradigm: (1) injury prevention, (2) immediate evaluation of treatment, and (3) long-term outcome evaluation. Related previous studies showing the approach in preventing and managing anterior cruciate ligament rupture and anterior talofibular ligament tear are highlighted. Orthopaedics and biomechanics specialists are encouraged to understand what they could contribute to the current and future practice of sports medicine

Transparent Authentication Utilising Gait Recognition

Securing smartphones has increasingly become inevitable due to their massive popularity and significant storage and access to sensitive information. The gatekeeper of securing the device is authenticating the user. Amongst the many solutions proposed, gait recognition has been suggested to provide a reliable yet non-intrusive authentication approach – enabling both security and usability. While several studies exploring mobile-based gait recognition have taken place, studies have been mainly preliminary, with various methodological restrictions that have limited the number of participants, samples, and type of features; in addition, prior studies have depended on limited datasets, actual controlled experimental environments, and many activities. They suffered from the absence of real-world datasets, which lead to verify individuals incorrectly. This thesis has sought to overcome these weaknesses and provide, a comprehensive evaluation, including an analysis of smartphone-based motion sensors (accelerometer and gyroscope), understanding the variability of feature vectors during differing activities across a multi-day collection involving 60 participants. This framed into two experiments involving five types of activities: standard, fast, with a bag, downstairs, and upstairs walking. The first experiment explores the classification performance in order to understand whether a single classifier or multi-algorithmic approach would provide a better level of performance. The second experiment investigated the feature vector (comprising of a possible 304 unique features) to understand how its composition affects performance and for a comparison a more particular set of the minimal features are involved. The controlled dataset achieved performance exceeded the prior work using same and cross day methodologies (e.g., for the regular walk activity, the best results EER of 0.70% and EER of 6.30% for the same and cross day scenarios respectively). Moreover, multi-algorithmic approach achieved significant improvement over the single classifier approach and thus a more practical approach to managing the problem of feature vector variability. An Activity recognition model was applied to the real-life gait dataset containing a more significant number of gait samples employed from 44 users (7-10 days for each user). A human physical motion activity identification modelling was built to classify a given individual's activity signal into a predefined class belongs to. As such, the thesis implemented a novel real-world gait recognition system that recognises the subject utilising smartphone-based real-world dataset. It also investigates whether these authentication technologies can recognise the genuine user and rejecting an imposter. Real dataset experiment results are offered a promising level of security particularly when the majority voting techniques were applied. As well as, the proposed multi-algorithmic approach seems to be more reliable and tends to perform relatively well in practice on real live user data, an improved model employing multi-activity regarding the security and transparency of the system within a smartphone. Overall, results from the experimentation have shown an EER of 7.45% for a single classifier (All activities dataset). The multi-algorithmic approach achieved EERs of 5.31%, 6.43% and 5.87% for normal, fast and normal and fast walk respectively using both accelerometer and gyroscope-based features – showing a significant improvement over the single classifier approach. Ultimately, the evaluation of the smartphone-based, gait authentication system over a long period of time under realistic scenarios has revealed that it could provide a secured and appropriate activities identification and user authentication system