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Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-bas...
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... problem. For more details, see [51]. The next step in our algorithm is the estimation of the vector T from the origin of the camera reference frame to a reference frame centered at the user's face. More precisely, the head pose reference system has its origin centered at the nose base and x-, y-axes parallel to the mouth and nose, respectively (Fig. 3). The rotation matrix R between the head pose and the camera reference system is pro- duced by IntraFace. In order to compute T, we assume a fixed distance D = 90 mm between the external corners of the eye contours and a fixed distance H = 70 mm from a point at the bottom of the nose (nose base) and the seg- ment joining the two pupils ...
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Eye trackers are currently used to sense the positions of both the centers of the pupils and the point-of-gaze (POG) position on a screen, which is the original objective for which they were designed; however, it is still difficult to measure the positions of three-dimensional (3D) POGs. This paper proposes a method for 3D gaze estimation by using...
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... Gaze estimation expresses feelings, desires, emotions, and in tension. A person-dependent calibration-free system could deal with the illumination [12]. Examining and establishing the variables that capture the changes caused by blinking in this article study. ...
... In appearance-based researches, literature [20] trained the machine learning algorithm by combining the azimuth information with the geometric features of the eyes of which the angle error of the gaze tracking was about 4°. In literature [18], a pupil detection algorithm based on color intensity change was used to detect the center of the pupil in order to estimate the user's sight. ...
Noncontact human-computer interaction has an important value in wireless sensor networks. This work is aimed at achieving accurate interaction on a computer based on auto eye control, using a cheap webcam as the video source. A real-time accurate human-computer interaction system based on eye state recognition, rough gaze estimation, and tracking is proposed. Firstly, binary classification of the eye states (opening or closed) is carried on using the SVM classification algorithm with HOG features of the input eye image. Second, rough appearance-based gaze estimation is implemented based on a simple CNN model. And the head pose is estimated to judge whether the user is facing the screen or not. Based on these recognition results, noncontact mouse control and character input methods are designed and developed to replace the standard mouse and keyboard hardware. Accuracy and speed of the proposed interaction system are evaluated by four subjects. The experimental results show that users can use only a common monocular camera to achieve gaze estimation and tracking and to achieve most functions of real-time precise human-computer interaction on the basis of auto eye control.
... Besides, the software platform has now also been more advanced and suitable for many applications. Due to these facts, research activities in machine vision applications (e.g., autonomous robots, biocybernetics, and biometric technology) have also increased drastically [1][2][3][4][5][6][7][8][9][10][11][12][13]. ...
... For example, authors in [34] utilize pupil constriction to differentiate genuine irises from fake ones. • Pupil localization has many applications in eye-gaze tracking [3,5,8,16,35,36]. It is the ability to notice the presence of visual attention from a subject and/or find a gaze-point (a point where someone is looking at by estimating his/her eye gaze. ...
Pupil detection in a human eyeimage or video plays a key role in many applications such as eye-tracking, diabetic retinopathy screening, smart homes, iris recognition, etc. Literature reveals pupil detection faces many complications including light reflections, cataract disease, pupil constriction/dilation moments, contact lenses, eyebrows, eyelashes, hair strips, and closed eye. To cope with these challenges, research community has been struggling to devise resilient pupil localization schemes for the image/video data collected using the near-infrared (NIR) or visible spectrum (VS) illumination. This study presents a critical review of numerous pupil detection schemes taken from standard sources. This review includes pupil localization schemes based on machine learning, histogram/thresholding, Integro-differential operator (IDO), Hough transform and among others. The probable pros and cons of each scheme are highlighted. Finally, this study offers recommendations for designing a robust pupil detection system. As scope of pupil detection is very broader, therefore this review would be a great source of information for the relevant research community.
... The linearity of the aforementioned scheme can offer interesting variations. In some case [122], geometric parameters can be used to compose the feature vector that is then used to obtain a direct mapping with the PoR: in our opinion, this potentially hybrid approach belongs to the category of appearance-based methods, because the output of parameter extraction represents full-fledged handcrafted features that are extracted to map a face/eye image with a gaze point, employing machine learning downstream. It is also important to note that the way the presence of the eyes is detected (eye detection/localization step) can differ from the gaze estimation step approach from a taxonomic viewpoint, but the gaze estimation method should still be classified, depending on how the PoR is effectively estimated, and not how the eye and pupil are extracted. ...
... A way to navigate the wide existing works is the presence or absence of a calibration phase. In fact, some works rely on a person dependent parameter estimation, thus a one-time personal calibration is necessary to obtain all of the parameters [128], while, in other cases, anatomic data [129] is either averaged [122], or person-independent constraints are introduced [130] in order to substitute the person-dependent calibration. ...
The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.
... Eye movements play a crucial role in terms of individual's perception and attention to the visual world [33]; consequently, non-intrusive eye detection and tracking have been investigated for decades in the development of human-computer interaction [34], attentive user interfaces [35], or cognitive behavioral therapy [36]. Eye-tracking is the measurement of eye movement/activity and gaze (point of regard) tracking is the analysis of eye tracking data with respect to the head/visual scene [37], and they have systematically been employed in healthcare applications [38]. ...
This paper gives an overview of the cutting-edge approaches that perform facial cue analysis in the healthcare area. The document is not limited to global face analysis but it also concentrates on methods related to local cues (e.g., the eyes). A research taxonomy is introduced by dividing the face in its main features: eyes, mouth, muscles, skin, and shape. For each facial feature, the computer vision-based tasks aiming at analyzing it and the related healthcare goals that could be pursued are detailed.
... e second component is the gaze estimation system, whose algorithm run on the same computer by taking in input images coming from a RGB camera, and outputs the user gaze with regards to the ve possible positions of the circle. e algorithm for the gaze estimation relies on a technique similar to the work proposed in [8]. A block diagram of the whole solution is shown in Fig. 1. e two following subsections will describe the proposed system in more detail. ...
... A random forest classi cator [5] trained on a labeled dataset is used to map head pose and pupil center information into one of the ve gaze point positions on the screen. About training data, the Data Set 2 introduced in [8] has been used. It consists of 1130 images of 10 di erent users whose gaze has been captured with di erent lighting conditions. ...
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disability characterized by difficulties in keeping concentration, excessive activity and difficulties controlling behaviour not appropriate to the person's age. It is estimated to affect between 4--9% of youths and 2--5% of adults. Assistive technologies can help people with ADHD to reach goals, stay organized and even fight the urge to succumb to forms of distraction. This work introduces a tool designed for people with ADHD aimed at detecting and training their ability to follow a target in a screen. The tool is based on non-invasive monocular gaze estimation technique without constraints in terms of user dependent calibration or appearance. The system has been employed and validated in a human-computer interaction (HCI) scenario with the aim of evaluating the user visual exploration. Results show that the tool can be used in complex tasks like monitoring a user progress comparing performance after different sessions.
In Controlling the mouse by a physically challenged person is really a tough one. To find a solution for the people who cannot use the Mouse physically, we have proposed this mouse cursor control using Eye Movements. Eye gaze is an alternative way of accessing a computer using eye movements to control the mouse. For someone who fine touch screens, mouse inaccessible, eye gaze is an alternative method to allow a user to operate their computer, using the movement of their eyes. Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system is proposed in this system using Webcam and without using any extra hardware. The proposed system focuses on providing a simple and convenient interactive mode by only using user’s eye. The usage flow of the proposed system is designed to perfectly follow human natural habits. The proposed system describes the implementation of both iris and movement of cursor according to iris position which can be used to control the cursor on the screen using webcam and implemented using Python
There is increasing evidence that eye trackers can be used in neuroscience research to assess cognitive function although expensive prices or low availability still prevent its ubiquitous application. Meanwhile, in tasks such as reaction speed testing in cognitive function assessment, the requirements for spatial performance are less critical than temporal resolution. Therefore, in this report we propose an eye-tracking system based on a common web-camera, to evaluate if it's possible to achieve a decent temporal resolution for eye-tracking tasks in cognitive experiments and then further evaluate it in Chinese reading tests. The performance of the proposed setup are evaluated in comparison experiments including 62 healthy subjects, with a commercialized eye-tracker (SMI RED250) simultaneously deployed. It’s found in the results that the webcam-based system showed no significant difference in the recognition accuracy of occurrence and switching of the eye movement events compared with the SMI system though the spatial eye-tracking performance is significantly worse. Then in an application experiment, cognitive-related Chinese reading tests are adapted and EMs of 27 young (18–30 years) and 28 elderly (over 60 years) readers are recorded to study their difference in Chinese reading. In the results there are significant differences in reading-time-per-sentence between young and elderly subjects. Meanwhile, a study on the reading-time-per-sentence difference using reading materials of different difficulties shows that elderly subjects are more sensitive to difficulty changes. These results preliminary demonstrated the feasibility of a Chinese reading test with a web-camera setup as an alternative option when the expensive eye-tracker is not available.
