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The hardware setup used for the screen registration. The stereo cameras are represented by two ellipses in front of the screen. Each object is shown with its own reference frame (w: world, m: mirror, v: virtual screen and s: screen).
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This paper describes a non-intrusive method to estimate the gaze direction of a person by using stereo cameras. First, facial features are tracked with an adapted particle filtering algorithm using factorized likelihoods to estimate the 3D head pose. Next the 3D gaze vector is calculated by estimating the eyeball center and the cornea center of bot...
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Context 1
... mirror is placed in front of the camera to capture the reflection of the screen. The camera will perceive this reflection as if another screen is located at the same distance from the mirror but in the opposite direction (see figure 5). We attached a reference frame to each of the objects: O w , O m , O v and O s for the world, mirror, virtual screen and the real screen frame, respectively. ...
Context 2
... displaying a calibration pattern on the screen, the virtual screen-to-world frame transformation M vw can be computed from the reflection of that pattern. With this information, we can choose three co-planar points and calculate their 3D world coordinates v w orig , v w long and v w short ( figure 5). Then, applying the following transformations to each of these points will result in the corresponding 3D screen points s w orig , s w long and s w short in world coordinates: ...
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... intersect the gaze ray with the screen we need the information about the screen location. In figure 15, the gaze direction is projected on the screen in point P . The V e c t o r -p l a n e i n t e r s e c t i o n 2 D S c r e e n c o o r d i n a t e V e c t o r -p l a n e i n t e r s e c t i o n A v e r a g e resulting gaze ray can be written in parametric representation as: ...
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The development of eye localization and gaze tracking systems without the constraint of dedicated hardware, has lately motivated the scientific community. A lot of "passive" methods have been proposed, however their precision is limited and significantly reduced compared to the commercial, hardware-based, eye tracking devices. In this paper we intr...
Citations
... Further to clarify some technical aspects, we browsed through literature related to eye detection [11] to better understand how the eye can be monitored to gain information about a person's attention towards something. Following that we read about eye detection used for drowsiness detection [12], and found that a person's eye movement or gaze tracking [13] can be helpful for monitoring attention levels. ...
As the youth mind rapidly evolves, education systems keep changing parallelly to remain effective. However, today’s education system focuses primarily on delivering knowledge but does not ensure it reaches the student. When student attentiveness is analysed, we can more efficiently evaluate the course structure and the needs of students. This is especially vital because E-learning is the future, but the inattentiveness of students and lack of feedback is holding back its growth. In order to overcome these problems, analysis of student attention in the classroom is done using computer vision. The behavior of students are captured with cameras fixed in the classroom and instance segmentation is applied to segment each student and determine his/her attentiveness. The analysis reports can be utilized by the course handlers to take further steps to improve the course material, and handle personal student issues. This feedback system aided by computer vision will take E-earning to the next level.KeywordsHuman behavior analysisE-learningComputer visionAttention estimationInstance segmentation
... One particle is sampled for each partition, such that the proposal function is maximized. PFFL was shown to be successful in tracking of facial features [11,13] and gaze tracking [14]. ...
... When partitions are allowed to translate independent of each other, their posterior distributions eventually drift away to states that are incompatible with a viable state of the object. On the other hand, partitions chosen on an object indeed move in a pattern, and in varying degrees of dependency with other partitions.In the literature partition dynamics has been modeled as Gaussian noise [11,21] and independent speeds [14]. Graph based models were proposed to incorporate temporal and spatial relations between feature points [22]. ...
... Pogalin [14] used the PFFL method and a 3D facial feature model as prior for gaze direction tracking. The head pose is estimated through tracked eye and mouth corners. ...
In particle filtering, dimensionality of the state space can be reduced by tracking control (or feature) points
as independent objects, which are traditionally named as partitions. Two critical decisions have to be made in implementation of reduced state-space dimensionality. First is how to construct a dynamic (transition) model for partitions that are inherently dependent. Second critical decision is how to filter partition states such that a viable and likely object state is achieved. In this study, we present a correlation-based transition model and a proposal function that incorporate partition dependency in particle filtering in a computationally tractable
manner. We test our algorithm on challenging examples of occlusion, clutter and drastic changes in relative speeds of partitions. Our successful results with as low as 10 particles per partition indicate that the proposed algorithm is both robust and efficient.
... Table II briefly outlines some of the approaches that have been introduced for measuring the performance of image processing-based gaze-trackers. For producing directly comparable evaluation results, the performance of the developed gaze-tracker was evaluated using the experimental protocols described in the works of [47][31] [48] [49][50] [51]. The comparative evaluation results given in Table II show that the developed gaze-tracker outperforms most state-of-art approaches. ...
In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user's relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of region-based RF approaches, i.e., the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.
... The overwhelming majority of eye gaze estimation approaches rely on "glints" -reflections of light off the cornea [5]. However, eye gaze may also be determined from pupil or iris contours [6], ellipse-fitting approaches [7], [8], the distance between the iris center and certain reference points (e.g., the eye corners) [9], [10], eye region segmentation and pixel-wise matching with 3D rendered eyeball models [11], [12], or even the reflection of the screen off the cornea [13]. Nonetheless, eye tracking in regular, non-infrared 2D imagery is still a difficult problem. ...
True immersion of a player within a game can only occur when the world simulated looks and behaves as close to reality as possible. This implies that the game must correctly read and understand, among other things, the player's focus, attitude toward the objects/persons in focus, gestures, and speech. In this paper, we proposed a novel system that integrates eye gaze estimation, head pose estimation, facial expression recognition, speech recognition, and text-to-speech components for use in real-time games. Both the eye gaze and head pose components utilize underlying 3-D models, and our novel head pose estimation algorithm uniquely combines scene flow with a generic head model. The facial expression recognition module uses the local binary patterns with three orthogonal planes approach on the 2-D shape index domain rather than the pixel domain, resulting in improved classification. Our system has also been extended to use a pan-tilt-zoom camera driven by the Kinect, allowing us to track a moving player. A test game, Art Critic, is also presented, which not only demonstrates the utility of our system but also provides a template for player/non-player character (NPC) interaction in a gaming context. The player alters his/her view of the 3-D world using head pose, looks at paintings/NPCs using eye gaze, and makes an evaluation based on the player's expression and speech. The NPC artist will respond with facial expression and synthetic speech based on its personality. Both qualitative and quantitative evaluations of the system are performed to illustrate the system's effectiveness.
... With a sufficiently large image of the eye, the iris contour and reflection of the screen off the cornea can be used to determine gaze [20]. One can also leverage the estimated iris center directly and use its distance from certain reference points (e.g., the eye corners) for gaze estimation [21], [22]. Indeed, the entire eye region may be segmented into the iris, sclera (white of the eye), and the surrounding skin; the resulting regions can then be matched pixel-wise with 3-D rendered eyeball models using different parameters [23], [24]. ...
In this paper, we present a vision-based human-computer interaction system, which integrates control components using multiple gestures, including eye gaze, head pose, hand pointing, and mouth motions. To track head, eye, and mouth movements, we present a two-camera system that detects the face from a fixed, wide-angle camera, estimates a rough location for the eye region using an eye detector based on topographic features, and directs another active pan-tilt-zoom camera to focus in on this eye region. We also propose a novel eye gaze estimation approach for point-of-regard (POR) tracking on a viewing screen. To allow for greater head pose freedom, we developed a new calibration approach to find the 3-D eyeball location, eyeball radius, and fovea position. Moreover, in order to get the optical axis, we create a 3-D iris disk by mapping both the iris center and iris contour points to the eyeball sphere. We then rotate the fovea accordingly and compute the final, visual axis gaze direction. This part of the system permits natural, non-intrusive, pose-invariant POR estimation from a distance without resorting to infrared or complex hardware setups. We also propose and integrate a two-camera hand pointing estimation algorithm for hand gesture tracking in 3-D from a distance. The algorithms of gaze pointing and hand finger pointing are evaluated individually, and the feasibility of the entire system is validated through two interactive information visualization applications.
... In [Pogalin 2004], the author determines 3D POR in world coordinates but within a specified 2D plane. Pogalin defines his gaze vector as starting at the center of the eye and pointing towards the center of the cornea. ...
To study an observer's eye movements during realistic tasks, the observer should be free to move naturally throughout our three-dimensional world. Therefore, a technique to determine an observer's point-of-regard (POR) as well as his/her motion throughout a scene in three dimensions with minor user input is proposed. This requires robust feature tracking and calibration of the scene camera in order to determine the 3D location and orientation of the scene camera in the world. With this information, calibrated 2D PORs can be triangulated to 3D positions in the world; the scale of the world coordinate system can be obtained via input of the distance between two known points in the scene. Information about scene camera movement and tracked features can also be used to obtain observer position and head orientation for all video frames. The final observer motion -- including the observer's positions and head orientations -- and PORs are expressed in 3D world coordinates. The result is knowledge of not only eye movements but head movements as well allowing for the evaluation of how an observer combines head and eye movements to perform a visual task. Additionally, knowledge of 3D information opens the door for many more options for visualization of eye-tracking results.
... Figure 1: Gradual Gaze-communication Model [1,12], perform special calibration processes [9,13,14], or use super-high-resolution images [20] . To overcome these problems , we propose a gaze-tracking method using a single remote camera [21,23]. ...
This paper proposes a gaze-communicative stuffed-toy robot system with joint attention and eye-contact reactions based on ambient gaze-tracking. For free and natural interaction, we adopted our remote gaze-tracking method. Corresponding to the user's gaze, the gaze-reactive stuffed-toy robot is designed to gradually establish 1) joint attention using the direction of the robot's head and 2) eye-contact reactions from several sets of motion. From both subjective evaluations and observations of the user's gaze in the demonstration experiments, we found that i) joint attention draws the user's interest along with the user-guessed interest of the robot, ii) "eye contact" brings the user a favorable feeling for the robot, and iii) this feeling is enhanced when "eye contact" is used in combination with "joint attention." These results support the approach of our embodied gaze-communication model.
The motion game controller project based on OpenCV technology enhances gaming experience. Also, it works with OpenCV where computer vision used in translating human hands movement to game plays. This one-of-a- kind controller has some degree of flexibility. The product lets you adjust the intensity of emotions to your individual needs and type of audio experience. A customized commodity promises a complete response tailored for different levels of skills available in the marketplace. Motion detection is reliable owing to OpenCV integration which ensures proper management in the game. The control center is suitable for any type of gaming whether it involves exploration of virtual worlds high-speed simulation or just mere playing.
Recent trends in the field of eye-gaze tracking have been shifting towards the estimation of gaze direction in everyday life settings, hence calling for methods that alleviate the constraints typically associated with existing methods, which limit their applicability in less controlled conditions. In this paper, we propose a method for eye-gaze estimation as a function of both eye and head pose components, without requiring prolonged user- cooperation prior to gaze estimation. Our method exploits the trajectories of salient feature trackers spread randomly over the face region for the estimation of the head rotation angles, which are subsequently used to drive a spherical eye-in-head rotation model that compensates for the changes in eye region appearance under head rotation. We investigate the validity of the proposed method on a publicly available data set.
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. | Post-print: http://www.stefaniacristina.engineer/publications/
