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 ...

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 ...

Automated analysis of facial expressions has been an active area of study due to its potential applications not only for intelligent human-computer interfaces but also for human facial behavior research. To advance automatic expression analysis, this thesis proposes and empirically proves two hypotheses: (i) 3D face data is a better data modality than conventional 2D camera images, not only for being much less disturbed by illumination and head pose effects but also for capturing true facial surface information. (ii) It is possible to perform detailed face registration without resorting to any face modeling. This means that data-driven methods in automatic expression analysis can compensate for the confounding effects like pose and physiognomy differences, and can process facial features more effectively, without suffering the drawbacks of model-driven analysis. Our study is based upon Facial Action Coding System (FACS) as this paradigm ...

Facial features segmentation, analysis and recognition of facial expressions by the Transferable Belief Model The aim of this work is the analysis and the classification of facial expressions. Experiments in psychology show that human is able to recognize the emotions based on the visualization of the temporal evolution of some characteristic fiducial points. Thus we firstly propose an automatic system for the extraction of the permanent facial features (eyes, eyebrows and lips). In this work we are interested in the problem of the segmentation of the eyes and the eyebrows. The segmentation of lips contours is based on a previous work developed in the laboratory. The proposed algorithm for eyes and eyebrows contours segmentation consists of three steps: firstly, the definition of parametric models to fit as accurate as possible the contour of each feature; then, a whole set of ...

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 ...

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson's, Alzheimer's, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients' anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long ...

Current Human-Machine Interfaces (HMI) lack of “emotional intelligence”, i.e. they are not able to identify human emotional states and take this information into account to decide on the proper actions to execute. The goal of affective computing is to fill this lack by detecting emotional cues occurring during Human-Computer Interaction (HCI) and synthesizing emotional responses. In the last decades, most of the studies on emotion assessment have focused on the analysis of facial expressions and speech to determine the emotional state of a person. Physiological activity also includes emotional information that can be used for emotion assessment but has received less attention despite of its advantages (for instance it can be less easily faked than facial expressions). This thesis reports on the use of two types of physiological activities to assess emotions in the context of affective computing: the activity ...

Computer vision-based analysis of human facial video provides information regarding to expression, diseases symptoms, and physiological parameters such as heartbeat rate, blood pressure and respiratory rate. It also provides a convenient source of heartbeat signal to be used in biometrics and forensics. This thesis is a collection of works done in five themes in the realm of computer vision-based facial image analysis: Monitoring elderly patients at private homes, Face quality assessment, Measurement of physiological parameters, Contact-free heartbeat biometrics, and Decision support system for healthcare. The work related to monitoring elderly patients at private homes includes a detailed survey and review of the monitoring technologies relevant to older patients living at home by discussing previous reviews and relevant taxonomies, different scenarios for home monitoring solutions for older patients, sensing and data acquisition techniques, data processing and analysis techniques, available datasets for ...

People of all generations are making more and more use of digital imaging systems in their daily lives. The image content rendered by these digital imaging systems largely differs in perceived quality depending on the system and its applications. To be able to optimize the experience of viewers of this content understanding and modeling perceived image quality is essential. Research on modeling image quality in a full-reference framework --- where the original content can be used as a reference --- is well established in literature. In many current applications, however, the perceived image quality needs to be modeled in a no-reference framework at real-time. As a consequence, the model needs to quantitatively predict perceived quality of a degraded image without being able to compare it to its original version, and has to achieve this with limited computational complexity in order ...

In this doctoral dissertation, we principally explore the use of the temporal information available in video sequences for person and gender recognition; in particular, we focus on the analysis of head and facial motion, and their potential application as biometric identifiers. We also investigate how to exploit as much video information as possible for the automatic recognition; more precisely, we examine the possibility of integrating the head and mouth motion information with facial appearance into a multimodal biometric system, and we study the extraction of novel spatio-temporal facial features for recognition. We initially present a person recognition system that exploits the unconstrained head motion information, extracted by tracking a few facial landmarks in the image plane. In particular, we detail how each video sequence is firstly pre-processed by semiautomatically detecting the face, and then automatically tracking the facial landmarks over ...

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 ...

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 ...

Machine learning and deep learning techniques have emerged as powerful tools for addressing complex challenges across diverse domains. These methodologies are powerful because they extract patterns and insights from large and complex datasets, automate decision-making processes, and continuously improve over time. They enable us to observe and quantify patterns in data that a normal human would not be able to capture, leading to deeper insights and more accurate predictions. This dissertation presents two research papers that leverage these methodologies to tackle distinct yet interconnected problems in neuroimaging and computer vision for the quantification of human health. The first investigation, "Age prediction using resting-state functional MRI," addresses the challenge of understanding brain aging. By employing the Least Absolute Shrinkage and Selection Operator (LASSO) on resting-state functional MRI (rsfMRI) data, we identify the most predictive correlations related to brain age. Our study, ...

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 ...