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 ...

Automatic ear recognition systems have seen increased interest over recent years due to multiple desirable characteristics. Ear images used in such systems can typically be extracted from profile head shots or video footage. The acquisition procedure is contactless and non-intrusive, and it also does not depend on the cooperation of the subjects. In this regard, ear recognition technology shares similarities with other image-based biometric modalities. Another appealing property of ear biometrics is its distinctiveness. Recent studies even empirically validated existing conjectures that certain features of the ear are distinct for identical twins. This fact has significant implications for security-related applications and puts ear images on a par with epigenetic biometric modalities, such as the iris. Ear images can also supplement other biometric modalities in automatic recognition systems and provide identity cues when other information is unreliable or even unavailable. In ...

This dissertation studies soft biometrics traits, their applicability in different security and commercial scenarios, as well as related usability aspects. We place the emphasis on human facial soft biometric traits which constitute the set of physical, adhered or behavioral human characteristics that can partially differentiate, classify and identify humans. Such traits, which include characteristics like age, gender, skin and eye color, the presence of glasses, moustache or beard, inherit several advantages such as ease of acquisition, as well as a natural compatibility with how humans perceive their surroundings. Specifically, soft biometric traits are compatible with the human process of classifying and recalling our environment, a process which involves constructions of hierarchical structures of different refined traits. This thesis explores these traits, and their application in soft biometric systems (SBSs), and specifically focuses on how such systems can achieve different goals ...

Face recognition is one of the most challenging problems of computer vision and pattern recognition. The difficulty in face recognition arises mainly from facial appearance variations caused by factors, such as expression, illumination, partial face occlusion, and time gap between training and testing data capture. Moreover, the performance of face recognition algorithms heavily depends on prior facial feature localization step. That is, face images need to be aligned very well before they are fed into a face recognition algorithm, which requires precise facial feature localization. This thesis addresses on solving these two main problems -facial appearance variations due to changes in expression, illumination, occlusion, time gap, and imprecise face alignment due to mislocalized facial features- in order to accomplish its goal of building a generic face recognition algorithm that can function reliably under real-world conditions. The proposed face recognition algorithm ...

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 ...

Interest in biometric based identification and verification systems has increased considerably over the last decade. As an example, the shortcomings of security systems based on passwords can be addressed through the supplemental use of biometric systems based on speech signals, face images or fingerprints. Biometric recognition can also be applied to other areas, such as passport control (immigration checkpoints), forensic work (to determine whether a biometric sample belongs to a suspect) and law enforcement applications (e.g. surveillance). While biometric systems based on face images and/or speech signals can be useful, their performance can degrade in the presence of challenging conditions. In face based systems this can be in the form of a change in the illumination direction and/or face pose variations. Multi-modal systems use more than one biometric at the same time. This is done for two main reasons -- ...

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 ...

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 ...

Nowadays, in a time where cities contain millions of people and where travelling across the world is becoming easier and easier, the necessity of automatically identifying a person is starting to be compelling. The physical appearance and the behavioural characteristics have been discovered useful to univocally describe a person. The analytic study of the human body measures with the aim of recognising or verifying the identity of a person, is called biometrics, literally "life measure". In the last century, several biometric traits have been investigated according to the most updated technologies available at the moment, improving recognition, computational time and memory capacity. Starting from the 90’s, research on biometrics has received a huge boost thanks to the interest raised by academic institutions, government agencies and private companies. Moreover, the diffusion of new instruments, able to perform faster analyses, and 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 ...

Biometric recognition for a long time has been used in confined spaces, usually indoor, where security-critical operations required high accuracy recognition systems, e.g. in police stations, banks, companies, airports. Field activities, on the contrary, required more portability and flexibility leading to the development of devices for less constrained biometric traits acquisition and consequently of robust algorithms for biometric recognition in less constrained conditions. However, the application of "portable" biometric recognition, was still limited in specific fields e.g. for immigration control, and still required dedicated devices. A further step would be to spread the use of biometric recognition on personal devices, as personal computers, tablets and smartphones. Some attempts in this direction were made embedding fingerprint scanners in laptops or smartphones. So far biometric recognition on personal devices has been employed just for a limited set of tasks, as to unlock ...

Face recognition has been an active area of study for both computer vision and image processing communities, not only for biometrics but also for human-computer interaction applications. The purpose of the present work is to evaluate the existing 3D face recognition techniques and seek biologically motivated methods to improve them. We especially look at findings in psychophysics and cognitive science for insights. We propose a biologically motivated computational model, and focus on the earlier stages of the model, whose performance is critical for the later stages. Our emphasis is on automatic localization of facial features. We first propose a strong unsupervised learning algorithm for flexible and automatic training of Gaussian mixture models and use it in a novel feature-based algorithm for facial fiducial point localization. We also propose a novel structural correction algorithm to evaluate the quality of landmarking and ...

Machine identification and recognition of human faces is a rapidly growing research area in both the academic and commercial world. Most of the research to date has concentrated on the use of two dimensional information, acquired from video cameras or photographs. The use of a three dimensional system is hoped to remove many of the problems affecting the two dimensional systems such as disruption caused by changes in the face’s orientation or changes in the ambient lighting. A three dimensional system will obviously not be influenced by orientation changes and the lighting is irrelevant, as it is the shape not the shading of the face that is important. For this system to be of practical use it is important that the process of acquiring the necessary information to generate the three dimensional surface model should not require any complex or ...

Automatic face recognition has attracted a lot of attention not only because of the large number of practical applications where human identification is needed but also due to the technical challenges involved in this problem: large variability in facial appearance, non-linearity of face manifolds and high dimensionality are some the most critical handicaps. In order to deal with the above mentioned challenges, there are two possible strategies: the first is to construct a “good” feature space in which the manifolds become simpler (more linear and more convex). This scheme usually comprises two levels of processing: (1) normalize images geometrically and photometrically and (2) extract features that are stable with respect to these variations (such as those based on Gabor filters). The second strategy is to use classification structures that are able to deal with non-linearities and to generalize properly. To ...