Gait can be defined as the individuals’ manner of walking. Its analysis can provide significant information about their identity and health, opening a wide range of possibilities in the field of biometric recognition and medical diagnosis. In the field of biometric, the use of gait to perform recognition can provide advantages, such as acquisition from a distance and without the cooperation of the individual being observed. In the field of medicine, gait analysis can be used to detect or assess the development of different gait related pathologies. It can also be used to assess neurological or systemic disorders as their effects are reflected in the individuals’ gait. This Thesis focuses on performing gait analysis in unconstrained environments, using a single 2D camera. This can be a challenging task due to the lack of depth information and self-occlusions in a 2D ...

Human recognition has become an important topic as the need and investments for security applications grow continuously. Biometrics enable reliable and efficient identity management systems by using physical and behavioral characteristics of the subjects that are permanent, universal and easy to access. This is why, the topic of biometrics attracts higher attention today. Numerous biometric systems exist which utilize various human characteristics. Among all biometrics traits, face recognition is advantageous in terms of accessibility and reliability. It allows identification at relatively high distances for unaware subjects that do not have to cooperate. In this dissertation, two challenges in face recognition are analyzed. The first one is face spoofing. Initially, spoofing in face recognition is explained together with the countermeasure techniques that are proposed for the protection of face recognition systems against spoofing attacks. The second challenge explored in this thesis ...

Face recognition is an important technology in computer vision, which often acts as an essential component in biometrics systems, HCI systems, access control systems, multimedia indexing applications, etc. In recent years, identification of subjects in non-controlled scenarios has received large amount of attentions from the biometrics research community. The deployment of real-time and robust face recognition systems can significantly reinforce the safety and security in public places or/and private residences. However, variations due to expressions/illuminations/poses/occlusions can significantly deteriorate the performance of face recognition systems in non-controlled environments. Partial occlusion, which significantly changes the appearance of part of a face, cannot only cause large performance deterioration of face recognition, but also can cause severe security issues. In this thesis, we focus on the occlusion problem in automatic face recognition in noncontrolled environments. Toward this goal, we propose a framework that consists ...

In a world where security issues have been gaining explosive importance, face and ear recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. While the recognition performance has been steadily improving, there are still challenging recognition scenarios and conditions, notably when facing large variations in the biometric data characteristics. Additionally, the widespread use of face and ear recognition solutions raises new security concerns, making the robustness against presentation attacks a very active field of research. Lenslet light field cameras have recently come into prominence as they are able to also capture the intensity of the light rays coming from multiple directions, thus offering a richer representation of the visual scene, notably spatio-angular information. To take benefit of this richer representation, light field cameras have recently been successfully applied, not only ...

The achievement of perfect security is out of the question. Even if we are not yet aware of them, every security aimed technology has weaknesses which attackers can exploit in order to circumvent the system. We should hence direct our efforts to the development of applications whose security level make it infeasible for computationally bound attackers to break the systems. This Thesis is focused on improving the security and privacy provided by biometric systems. With the increased need for reliable and automatic identity verification, biometrics have emerged in the last decades as a pushing alternative to traditional authentication methods. Certainly, biometrics are very attractive and useful for the general public: forget about PINs and passwords, you are your own key. However, the wide deployment of biometric recognition systems at both large-scale applications (e.g., border management at European level or national ...

This Thesis is focused on the quality assessment of biometric signals and its application to multimodal biometric systems. Since the establishment of biometrics as an specific research area in late 90s, the biometric community has focused its efforts in the development of accurate recognition algorithms and nowadays, biometric recognition is a mature technology that is used in many applications. However, we can notice recent studies that demonstrate how performance of biometric systems is heavily affected by the quality of biometric signals. Quality measurement has emerged in the biometric community as an important concern after the poor performance observed in biometric systems on certain pathological samples. We first summarize the state-of-the-art in the biometric quality problem. We present the factors influencing biometric quality, which mainly have to do with four issues: the individual itself, the sensor used in the acquisition, the ...

The number of cameras increases rapidly in squares, shopping centers, railway stations and airport halls. There are hundreds of cameras in the city center of Amsterdam. This is still modest compared to the tens of thousands of cameras in London, where citizens are expected to be filmed by more than three hundred cameras of over thirty separate Closed Circuit Television (CCTV) systems in a single day [84]. These CCTV systems include both publicly owned systems (railway stations, squares, airports) and privately owned systems (shops, banks, hotels). The main purpose of all these cameras is to detect, prevent and monitor crime and anti-social behaviour. Other goals of camera surveillance can be detection of unauthorized access, improvement of service, fire safety, etc. Since the terrorist attack on 9/11, detection and prevention of terrorist activities especially at high profiled locations such as airports, ...

Absolute security does not exist: given funding, willpower and the proper technology, every security system can be compromised. However, the objective of the security community should be to develop such applications that the funding, the will, and the resources needed by the attacker to crack the system prevent him from attempting to do so. This Thesis is focused on the vulnerability assessment of biometric systems. Although being relatively young compared to other mature and long-used security technologies, biometrics have emerged in the last decade as a pushing alternative for applications where automatic recognition of people is needed. Certainly, biometrics are very attractive and useful for the final user: forget about PINs and passwords, you are your own key. However, we cannot forget that as any technology aimed to provide a security service, biometric systems are exposed to external attacks which ...

Epilepsy is one of the most common neurological disorders, which affects almost 1% of the population worldwide. Anti-epileptic drugs provide adequate treatment for about 70% of epilepsy patients. The remaining 30% of the patients continue to have seizures, which drastically affects their quality of life. In order to obtain efficacy measures of therapeutic interventions for these patients, an objective way to count and document seizures is needed. However, in an outpatient setting, one of the major problems is that seizure diaries kept by patients are unreliable. Automated seizure detection systems could help to objectively quantify seizures. Those detection systems are typically based on full scalp Electroencephalography (EEG). In an outpatient setting, full scalp EEG is of limited use because patients will not tolerate wearing a full EEG cap for long time periods during daily life. There is a need for ...

This Thesis is focused on dealing with the variability factors in biometric recognition and applications of biometrics at a distance. In particular, this PhD Thesis explores the problem of variability factors assessment and how to deal with them by the incorporation of soft biometrics information in order to improve person recognition systems working at a distance. The proposed methods supported by experimental results show the benefits of adapting the system considering the variability of the sample at hand. Although being relatively young compared to other mature and long-used security technologies, biometrics have emerged in the last decade as a pushing alternative for applications where automatic recognition of people is needed. Certainly, biometrics are very attractive and useful for video surveillance systems at a distance, widely distributed in our lifes, and for the final user: forget about PINs and passwords, you ...

This thesis addresses the problem of vision based sign language recognition and focuses on three main tasks to design improved techniques that increase the performance of sign language recognition systems. We first attack the markerless tracking problem during natural and unrestricted signing in less restricted environments. We propose a joint particle filter approach for tracking multiple identical objects, in our case the two hands and the face, which is robust to situations including fast movement, interactions and occlusions. Our experiments show that the proposed approach has a robust tracking performance during the challenging situations and is suitable for tracking long durations of signing with its ability of fast recovery. Second, we attack the problem of the recognition of signs that include both manual (hand gestures) and non-manual (head/body gestures) components. We investigated multi-modal fusion techniques to model the different temporal ...

The increasing need for personal authentication in many daily applications has made biometrics a fundamental research area. In particular, handwritten signatures have long been considered one of the most valuable biometric traits. Signatures are the most popular method for identity verification all over the world, and people are familiar with the use of signatures for identity verification purposes in their everyday life. In fact, signatures are widely used in several daily transactions, being recognized as a legal means of verifying an individual’s identity by financial and administrative institutions. In addition, signature verification has the advantage of being a non-invasive biometric technique. Two categories of signature verification systems can be distinguished taking into account the acquisition device, namely, offline systems, where only the static image of the signature is available, and online systems, where dynamic information acquired during the signing process, ...

The field of Life Science Engineering (LSE) is rapidly expanding and predicted to grow strongly in the next decades. It covers areas of food and medical research, plant and pests’ research, and environmental research. In each research area, engineers try to find equations that model a certain life science problem. Once found, they research different numerical techniques to solve for the unknown variables of these equations. Afterwards, solution improvement is examined by adopting more accurate conventional techniques, or developing novel algorithms. In particular, signal and image processing techniques are widely used to solve those LSE problems require pattern recognition. However, due to the continuous evolution of the life science problems and their natures, these solution techniques can not cover all aspects, and therefore demanding further enhancement and improvement. The thesis presents numerical algorithms of digital signal and image processing to ...

Recently, significant advances in the field of automated face recognition have been achieved using computer vision, machine learning, and deep learning methodologies. However, despite claims of super-human performance of face recognition algorithms on select key benchmark tasks, there remain several open problems that preclude the general replacement of human face recognition work with automated systems. State-of-the-art automated face recognition systems based on deep learning methods are able to achieve high accuracy when the face images they are tasked with recognizing subjects from are of sufficiently high quality. However, low image resolution remains one of the principal obstacles to face recognition systems, and their performance in the low-resolution regime is decidedly below human capabilities. In this PhD thesis, we present a systematic study of modern automated face recognition systems in the presence of image degradation in various forms. Based on our ...

The explosion of Deep Learning (DL) added a boost to the already rapidly developing field of Computer Vision to such a point that vision-based tasks are now parts of our everyday lives. Applications such as image classification, photo stylization, or face recognition are nowadays pervasive, as evidenced by the advent of modern systems trivially integrated into mobile applications. In this thesis, we investigated and enhanced the visual counting task, which automatically estimates the number of objects in still images or video frames. Recently, due to the growing interest in it, several Convolutional Neural Network (CNN)-based solutions have been suggested by the scientific community. These artificial neural networks, inspired by the organization of the animal visual cortex, provide a way to automatically learn effective representations from raw visual data and can be successfully employed to address typical challenges characterizing this task, ...