Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

¿Qué dicen sus ojos? Conectando los movimientos oculares hacia el comportamiento del consumidor

Eye tracking (ET) is a technique that has been progressively employed to study the influence of visual stimuli on attentional processes and consumer behavior. The goals of the present theoretical article are fourfold and are based on an extensive literature revision. First, a brief historical review of ET methodology is introduced, presenting the evolution of ET techniques from the ancient proto-eye trackers to the "fresh" state-of-the-art eye ET devices. Second, the basics of ET are clarified through a simplified technical and mathematical explanation. Third, the triad eye movement-attention-consumer behavior is made clear, grounded on attention, interest, desire, and action (AIDA) theoretical model. Fourth, the most used oculometrics in marketing studies are explained and distinguished The present article addresses a number of technical and methodological issues by discussing challenges involved in ET systems and giving some guidelines for those who intend to apply ET to infer cognitive and emotional processes.info:eu-repo/semantics/publishedVersio

Leveraging Eye Structure and Motion to Build a Low-Power Wearable Gaze Tracking System

Clinical studies have shown that features of a person\u27s eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders such as Parkinson\u27s, autism, and schizophrenia. While there is a wealth of knowledge and social benefit to be gained from eye tracking, the field has historically been restricted to laboratory use by crippling technological limitations - most notably, device size and power consumption. These issues primarily stem from the use of high-resolution cameras and heavyweight video-processing algorithms, both of which induce extremely high performance overhead on the eye tracker. To address this problem, we have constructed a lightweight, ultra-low-power eye monitoring device in the form factor of a pair of eyeglasses. The key guiding design principle for its construction was saliency-aware resource minimization. Specifically, our design leverages the fact that close-up images of the eye are characterized by large salient features which provide a high degree of redundant information; we exploit this to heavily subsample the eye image and reduce resource utilization while performing effective eye tracking. In the first part of this thesis, we present an initial design of a wearable system to enable ubiquitous eye tracking. By exploiting the fact that the eye has several large, visually redundant features such as the iris and pupil, we were able to develop a neural-network-based adaptive-sampling algorithm for predicting the gaze point while sampling a minimal number of pixels from the image. This enabled us to realize a power savings using specialized imaging hardware that would sample only those most salient pixels, which proportionally reduced the power and time cost of reading images for eye tracking. With these optimizations we were able to build a first-of-of its kind wearable eye tracker that consumed 40 mW of power and demonstrated a gaze tracking error of only 3 degrees across multiple subjects. We refer to this device as the iShadow platform. The second contribution and section of this thesis is a significant improvement upon the original iShadow design for the purpose of improving both power utilization and eye tracking performance. We constructed a new pupil-tracking algorithm based on lightweight computer vision features, which leverages the smoothness of the eye\u27s motion to reduce even further the amount of camera sampling needed. To guard against very infrequent discontinuities resulting from blinks or reflections off the eye, we integrated this model with the previously-used one-shot neural network algorithm. Because the common case (smooth, uninterrupted eye motion) occurs 90% of the time, we were able to realize a dramatic increase in performance due to the efficiency of the smooth tracking algorithm. The new and improved system, labeled CIDER, enabled much more accurate eye tracking - 0.4 degree error - with power consumption as low as 7 mW. This design also enabled a tradeoff between power consumption and eye tracking rate, in which it was also possible to draw higher power of ~30 mW in order to do eye tracking at rates of up to 240 frames per second. The final contribution of this thesis is a re-designed version of the iShadow glasses hardware that is suitable for ``in-the-wild\u27\u27 studies on subjects in their daily living environment. A wearable device, especially one that is worn on the head, must be minimally obtrusive in order to be accepted and used in the field by subjects. This design goal conflicts with the ideal placement of cameras that is needed for achieving consistent eye tracking fidelity. We present multiple possible methods we explored for addressing these competing design challenges, and discuss the reasons that many proved infeasible. To conclude, we present a working design solution that appears to optimally trade off user comfort and convenience and against the technical requirements of the system

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Design Principles for Special Purpose, Embodied, Conversational Intelligence with Environmental Sensors (SPECIES) Agents

As information systems increase their ability to gather and analyze data from the natural environment and as computational power increases, the next generation of human-computer interfaces will be able to facilitate more lifelike and natural interactions with humans. This can be accomplished by using sensors to non-invasively gather information from the user, using artificial intelligence to interpret this information to perceive users’ emotional and cognitive states, and using customized interfaces and responses based on embodied-conversational-agent (avatar) technology to respond to the user. We refer to this novel and unique class of intelligent agents as Special Purpose Embodied Conversational Intelligence with Environmental Sensors (SPECIES) agents. In this paper, we build on interpersonal communication theory to specify four essential design principles of all SPECIES agents. We also share findings of initial research that demonstrates how SPECIES agents can be deployed to augment human tasks. Results of this paper organize future research efforts in collectively studying and creating more robust, influential, and intelligent SPECIES agents

Low-cost eye-tracking for human computer interaction

Knowing the user\u27s point of gaze has long held the promise of being a useful methodology for human computer interaction. However, a number of barriers have stood in the way of the integration of eye tracking into everyday applications, including the intrusiveness, robustness, availability, and price of eye-tracking systems. The goal of this thesis is to lower these barriers so that eye tracking can be used to enhance current human computer interfaces. An eye-tracking system was developed. The system consists of an open-hardware design for a digital eye tracker that can be built from low-cost off-the-shelf components, and a set of open-source software tools for digital image capture, manipulation, and analysis in eye-tracking applications. Both infrared and visible spectrum eye-tracking algorithms were developed and used to calculate the user\u27s point of gaze in two types of eye tracking systems, head-mounted and remote eye trackers. The accuracy of eye tracking was found to be approximately one degree of visual angle. It is expected that the availability of this system will facilitate the development of eye-tracking applications and the eventual integration of eye tracking into the next generation of everyday human computer interfaces

Applying Eye Tracking in Information Security

Abstract In this paper, we are considering the possibility of using eye tracking technology in the context of information security. We expect that this novel technique will have a very effective impact in this field. Eye tracking technology is widely used to investigate user behavior when working with a computer. This technology allows to obtain analysis in the form fixation point (location of a user's eye gaze), scan-path (gaze trajectory), heat maps (areas of interest), salient picture components. Eye tracking research has been widely used to improve the design and usability of web pages, as well as to explore an understanding how users are guided by them. Moreover, this technology is widely used in the protection of information

iMind: Uma ferramenta inteligente para suporte de compreensão de conteúdo

Usually while reading, content comprehension difficulty affects individual performance. Comprehension difficulties, e. g., could lead to a slow learning process, lower work quality, and inefficient decision-making. This thesis introduces an intelligent tool called “iMind” which uses wearable devices (e.g., smartwatches) to evaluate user comprehension difficulties and engagement levels while reading digital content. Comprehension difficulty can occur when there are not enough mental resources available for mental processing. The mental resource for mental processing is the cognitive load (CL). Fluctuations of CL lead to physiological manifestation of the autonomic nervous system (ANS), which can be measured by wearables, like smartwatches. ANS manifestations are, e. g., an increase in heart rate. With low-cost eye trackers, it is possible to correlate content regions to the measurements of ANS manifestation. In this sense, iMind uses a smartwatch and an eye tracker to identify comprehension difficulty at content regions level (where the user is looking). The tool uses machine learning techniques to classify content regions as difficult or non-difficult based on biometric and non-biometric features. The tool classified regions with a 75% accuracy and 80% f-score with Linear regression (LR). With the classified regions, it will be possible, in the future, to create contextual support for the reader in real-time by, e.g., translating the sentences that induced comprehension difficulty.Normalmente durante a leitura, a dificuldade de compreensão pode afetar o desempenho da leitura. A dificuldade de compreensão pode levar a um processo de aprendizagem mais lento, menor qualidade de trabalho ou uma ineficiente tomada de decisão. Esta tese apresenta uma ferramenta inteligente chamada “iMind” que usa dispositivos vestíveis (por exemplo, smartwatches) para avaliar a dificuldade de compreensão do utilizador durante a leitura de conteúdo digital. A dificuldade de compreensão pode ocorrer quando não há recursos mentais disponíveis suficientes para o processamento mental. O recurso usado para o processamento mental é a carga cognitiva (CL). As flutuações de CL levam a manifestações fisiológicas do sistema nervoso autônomo (ANS), manifestações essas, que pode ser medido por dispositivos vestíveis, como smartwatches. As manifestações do ANS são, por exemplo, um aumento da frequência cardíaca. Com eye trackers de baixo custo, é possível correlacionar manifestação do ANS com regiões do texto, por exemplo. Neste sentido, a ferramenta iMind utiliza um smartwatch e um eye tracker para identificar dificuldades de compreensão em regiões de conteúdo (para onde o utilizador está a olhar). Adicionalmente a ferramenta usa técnicas de machine learning para classificar regiões de conteúdo como difíceis ou não difíceis com base em features biométricos e não biométricos. A ferramenta classificou regiões com uma precisão de 75% e f-score de 80% usando regressão linear (LR). Com a classificação das regiões em tempo real, será possível, no futuro, criar suporte contextual para o leitor em tempo real onde, por exemplo, as frases que induzem dificuldade de compreensão são traduzidas

Face pose estimation with automatic 3D model creation for a driver inattention monitoring application

Texto en inglés y resumen en inglés y españolRecent studies have identified inattention (including distraction and drowsiness) as the main cause of accidents, being responsible of at least 25% of them. Driving distraction has been less studied, since it is more diverse and exhibits a higher risk factor than fatigue. In addition, it is present over half of the inattention involved crashes. The increased presence of In Vehicle Information Systems (IVIS) adds to the potential distraction risk and modifies driving behaviour, and thus research on this issue is of vital importance. Many researchers have been working on different approaches to deal with distraction during driving. Among them, Computer Vision is one of the most common, because it allows for a cost effective and non-invasive driver monitoring and sensing. Using Computer Vision techniques it is possible to evaluate some facial movements that characterise the state of attention of a driver. This thesis presents methods to estimate the face pose and gaze direction of a person in real-time, using a stereo camera as a basic for assessing driver distractions. The methods are completely automatic and user-independent. A set of features in the face are identified at initialisation, and used to create a sparse 3D model of the face. These features are tracked from frame to frame, and the model is augmented to cover parts of the face that may have been occluded before. The algorithm is designed to work in a naturalistic driving simulator, which presents challenging low light conditions. We evaluate several techniques to detect features on the face that can be matched between cameras and tracked with success. Well-known methods such as SURF do not return good results, due to the lack of salient points in the face, as well as the low illumination of the images. We introduce a novel multisize technique, based on Harris corner detector and patch correlation. This technique benefits from the better performance of small patches under rotations and illumination changes, and the more robust correlation of the bigger patches under motion blur. The head rotates in a range of ±90º in the yaw angle, and the appearance of the features change noticeably. To deal with these changes, we implement a new re-registering technique that captures new textures of the features as the face rotates. These new textures are incorporated to the model, which mixes the views of both cameras. The captures are taken at regular angle intervals for rotations in yaw, so that each texture is only used in a range of ±7.5º around the capture angle. Rotations in pitch and roll are handled using affine patch warping. The 3D model created at initialisation can only take features in the frontal part of the face, and some of these may occlude during rotations. The accuracy and robustness of the face tracking depends on the number of visible points, so new points are added to the 3D model when new parts of the face are visible from both cameras. Bundle adjustment is used to reduce the accumulated drift of the 3D reconstruction. We estimate the pose from the position of the features in the images and the 3D model using POSIT or Levenberg-Marquardt. A RANSAC process detects incorrectly tracked points, which are not considered for pose estimation. POSIT is faster, while LM obtains more accurate results. Using the model extension and the re-registering technique, we can accurately estimate the pose in the full head rotation range, with error levels that improve the state of the art. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area, parameter which gives much information about the distraction pattern of the driver. The resulting gaze estimation algorithm proposed in this thesis has been tested on a set of driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests. The driver?s fixation statistics obtained with the proposed system show how the utilisation of IVIS influences the distraction pattern of the drivers, increasing reaction times and affecting the fixation of attention on the road and the surroundings