19,594 research outputs found

    3D Face tracking and gaze estimation using a monocular camera

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    Estimating a user’s gaze direction, one of the main novel user interaction technologies, will eventually be used for numerous applications where current methods are becoming less effective. In this paper, a new method is presented for estimating the gaze direction using Canonical Correlation Analysis (CCA), which finds a linear relationship between two datasets defining the face pose and the corresponding facial appearance changes. Afterwards, iris tracking is performed by blob detection using a 4-connected component labeling algorithm. Finally, a gaze vector is calculated based on gathered eye properties. Results obtained from datasets and real-time input confirm the robustness of this metho

    The value of eye-tracking technology in the analysis and interpretations of skeletal remains: A pilot study

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    This initial study is the first to use eye-trackers as a tool in order to study gaze pattern strategies and decision making processes involved in the assessment of skeletal remains. Three experienced participants were asked to wear eye-tracking glasses (Tobii Pro Glasses 2) when estimating sex and age-at-death of one set of skeletal remains from a known archeological sample. The study assessed participants' fixation points (the features of the skeleton focused on), fixation duration (the total time spent on each assessment and feature) as well as visit count and duration (the total number of visits and the duration of visits to particular areas). The preliminary results of this study identified differences in gaze “strategies” with regards to fixation points, visit duration, and visit counts between the participants. The data generated provide a starting point for assessing how such technologies could be used in order to more fully understand the decision making processes involved in forensic anthropological interpretations and their role in forensic reconstructions

    Algorithms for High-Speed Noninvasive Eye-Tracking System

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    Two image-data-processing algorithms are essential to the successful operation of a system of electronic hardware and software that noninvasively tracks the direction of a person s gaze in real time. The system was described in High-Speed Noninvasive Eye-Tracking System (NPO-30700) NASA Tech Briefs, Vol. 31, No. 8 (August 2007), page 51. To recapitulate from the cited article: Like prior commercial noninvasive eyetracking systems, this system is based on (1) illumination of an eye by a low-power infrared light-emitting diode (LED); (2) acquisition of video images of the pupil, iris, and cornea in the reflected infrared light; (3) digitization of the images; and (4) processing the digital image data to determine the direction of gaze from the centroids of the pupil and cornea in the images. Most of the prior commercial noninvasive eyetracking systems rely on standard video cameras, which operate at frame rates of about 30 Hz. Such systems are limited to slow, full-frame operation. The video camera in the present system includes a charge-coupled-device (CCD) image detector plus electronic circuitry capable of implementing an advanced control scheme that effects readout from a small region of interest (ROI), or subwindow, of the full image. Inasmuch as the image features of interest (the cornea and pupil) typically occupy a small part of the camera frame, this ROI capability can be exploited to determine the direction of gaze at a high frame rate by reading out from the ROI that contains the cornea and pupil (but not from the rest of the image) repeatedly. One of the present algorithms exploits the ROI capability. The algorithm takes horizontal row slices and takes advantage of the symmetry of the pupil and cornea circles and of the gray-scale contrasts of the pupil and cornea with respect to other parts of the eye. The algorithm determines which horizontal image slices contain the pupil and cornea, and, on each valid slice, the end coordinates of the pupil and cornea. Information from multiple slices is then combined to robustly locate the centroids of the pupil and cornea images. The other of the two present algorithms is a modified version of an older algorithm for estimating the direction of gaze from the centroids of the pupil and cornea. The modification lies in the use of the coordinates of the centroids, rather than differences between the coordinates of the centroids, in a gaze-mapping equation. The equation locates a gaze point, defined as the intersection of the gaze axis with a surface of interest, which is typically a computer display screen (see figure). The expected advantage of the modification is to make the gaze computation less dependent on some simplifying assumptions that are sometimes not accurat

    Unobtrusive and pervasive video-based eye-gaze tracking

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    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

    Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction

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    The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model. The proposed formulation leads to a tractable learning procedure and to an efficient algorithm that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets that contain typical multi-party human-robot and human-human interactions.Comment: 15 pages, 8 figures, 6 table

    I Can See Your Aim: Estimating User Attention From Gaze For Handheld Robot Collaboration

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    This paper explores the estimation of user attention in the setting of a cooperative handheld robot: a robot designed to behave as a handheld tool but that has levels of task knowledge. We use a tool-mounted gaze tracking system, which, after modelling via a pilot study, we use as a proxy for estimating the attention of the user. This information is then used for cooperation with users in a task of selecting and engaging with objects on a dynamic screen. Via a video game setup, we test various degrees of robot autonomy from fully autonomous, where the robot knows what it has to do and acts, to no autonomy where the user is in full control of the task. Our results measure performance and subjective metrics and show how the attention model benefits the interaction and preference of users.Comment: this is a corrected version of the one that was published at IROS 201
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