In this thesis, we attack the problem of identifying humans from their three dimensional facial characteristics. For this purpose, a complete 3D face recognition system is developed. We divide the whole system into sub-processes. These sub-processes can be categorized as follows: 1) registration, 2) representation of faces, 3) extraction of discriminative features, and 4) fusion of matchers. For each module, we evaluate the state-of-the art methods, and also propose novel ones. For the registration task, we propose to use a generic face model which speeds up the correspondence establishment process. We compare the benefits of rigid and non-rigid registration schemes using a generic face model. In terms of face representation schemes, we implement a diverse range of approaches such as point clouds, curvature-based descriptors, and range images. In relation to these, various feature extraction methods are used to determine the ...

This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. We have collected two datasets including facial and head movement measures. Head motion is collected through an accelerometer for the first dataset (UYAN-1) and an ...

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 ...

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Lo´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

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 analysis of head gestures and facial expressions is a challenging research area and it has significant applications for intelligent human-computer interfaces. An important task is the automatic classification of non-verbal messages composed of facial signals where both facial expressions and head rotations are observed. This is a challenging task, because there is no definite grammar or code-book for mapping the non-verbal facial signals into a corresponding mental state. Furthermore, non-verbal facial signals and the observed emotions have dependency on personality, society, state of the mood and also the context in which they are displayed or observed. This thesis mainly addresses the three desired tasks for an effective visual information based automatic face and head gesture (FHG) analyzer. First we develop a fully automatic, robust and accurate 17-point facial landmark localizer based on local appearance information and structural information of ...

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 ...

Heavy demands on the development of medical imaging modalities for breast cancer detection have been witnessed in the last three decades in an attempt to reduce the mortality associated with the disease. Recently, Digital Breast Tomosynthesis (DBT) shows its promising in the early diagnosis when lesions are small. In particular, it offers potential benefits over X-ray mammography - the current modality of choice for breast screening - of increased sensitivity and specificity for comparable X-ray dose, speed, and cost. An important feature of DBT is that it provides a pseudo-3D image of the breast. This is of particular relevance for heterogeneous dense breasts of young women, which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird from the edge of the forest, detecting cancer in a conventional 2D mammogram ...

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 ...

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 ...

Communicating with mobile devices has become an unavoidable part of our daily life. Unfortunately, the current user interface designs are mostly taken directly from desktop computers. This has resulted in devices that are sometimes hard to use. Since more processing power and new sensing technologies are already available, there is a possibility to develop systems to communicate through different modalities. This thesis proposes some novel computer vision approaches, including head tracking, object motion analysis and device ego-motion estimation, to allow efficient interaction with mobile devices. For head tracking, two new methods have been developed. The first method detects a face region and facial features by employing skin detection, morphology, and a geometrical face model. The second method, designed especially for mobile use, detects the face and eyes using local texture features. In both cases, Kalman filtering is applied to estimate ...

One desideratum in designing cognitive robots is autonomous learning of communication skills, just like humans. The primary step towards this goal is vocabulary acquisition. Being different from the training procedures of the state-of-the-art automatic speech recognition (ASR) systems, vocabulary acquisition cannot rely on prior knowledge of language in the same way. Like what infants do, the acquisition process should be data-driven with multi-level abstraction and coupled with multi-modal inputs. To avoid lengthy training efforts in a word-by-word interactive learning process, a clever learning agent should be able to acquire vocabularies from continuous speech automatically. The work presented in this thesis is entitled \emph{Constrained Non-negative Matrix Factorization for Vocabulary Acquisition from Continuous Speech}. Enlightened by the extensively studied techniques in ASR, we design computational models to discover and represent vocabularies from continuous speech with little prior knowledge of the language to ...

This research aims at supporting users if not involved in computer graphics, facial physiology, or psychology and in need of generating realistic facial animations. Realism is to be understood in terms of the visual appeal of a single rendered image and focused on believable behaviour of the animated face. Our goal is to develop a system enabling semi-automatic facial animation, allowing an average user to generate facial animation in a simple manner. A system with knowledge about the communicative functions of facial expressions that would support an average user to generate facial animation valid from a psychological and physiological point of view.

Shape description is a crucial step in many computer vision applications. This thesis is an attempt to introduce various representations of two and three dimensional shape information. These representations are aimed to be in homogeneous parametric forms in 2D or 3D space, such that subspace-based feature extraction techniques are applicable on them. We tackle three di erent applications: (i) Person recognition with hand biometry, (ii) Person recognition with three-dimensional face biometry, (iii) Indexing and retrieval of generic three-dimensional models. For each application, we propose various combinations of shape representation schemes and subspace-based feature extraction methods. We consider subspaces with fixed bases such as cosines, complex exponentials and tailored subspaces such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization. Most of the descriptors we propose are dependent on the pose of the object. In this thesis we give ...

The growing interest in neuroimaging technologies generates a massive amount of biomedical data that exhibit high dimensionality. Tensor-based analysis of brain imaging data has by now been recognized as an effective approach exploiting its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization; the identification of the regions of the brain which are activated at specific time instances. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data. In the first part of this thesis, we aimed at investigating the possible gains from exploiting the 3-dimensional nature of the brain images, through a higher-order tensorization ...