text: Estimating human gaze from natural eye images only is a challenging task. Gaze direction can be defined by the pupil- and the eyeball center where the latter is unobservable in 2D images. Hence, achieving highly accurate gaze estimates is an ill-posed problem. In this paper, we introduce a novel deep neural network architecture specifically designed for the task of gaze estimation from single eye input. Instead of directly regressing two angles for the pitch and yaw of the eyeball, we regress to an intermediate pictorial representation which in turn simplifies the task of 3D gaze direction estimation. Our quantitative and qualitative results show that our approach achieves higher accuracies than the state-of-the-art and is robust to variation in gaze, head pose and image quality.
  @cite_26: Despite the widespread use of mobile phones and tablets, hand-held portable devices have only recently been identified as a promising platform for gaze-aware applications. Estimating gaze on portable devices is challenging given their limited computational resources, low quality integrated front-facing RGB cameras, and small screens to which gaze is mapped. In this paper we present EyeTab, a model-based approach for binocular gaze estimation that runs entirely on an unmodified tablet. EyeTab builds on set of established image processing and computer vision algorithms and adapts them for robust and near-realtime gaze estimation. A technical prototype evaluation with eight participants in a normal indoors office setting shows that EyeTab achieves an average gaze estimation accuracy of 6.88° of visual angle at 12 frames per second.
  @cite_7: Simple-setup real-time gaze estimation system using only a consumer depth camera.3D model based gaze estimation method allowing free head movement.Iris center localization method able to handle relatively low-quality eye images.Simple system calibration easy to be carried out for nonprofessional users. Existing eye-gaze-tracking systems typically require multiple infrared (IR) lights and high-quality cameras to achieve good performance and robustness against head movement. This requirement limits the systems' potential for broader applications. In this paper, we present a low-cost, non-intrusive, simple-setup gaze estimation system that can estimate the gaze direction under free head movement. In particular, the proposed system only uses a consumer depth camera (Kinect sensor) positioned at a distance from the subject. We develop a simple procedure to calibrate the geometric relationship between the screen and the camera, and subject-specific parameters. A parameterized iris model is then used to locate the center of the iris for gaze feature extraction, which can handle low-quality eye images. Finally, the gaze direction is determined based on a 3D geometric eye model, where the head movement and deviation of the visual axis from the optical axis are taken into consideration. Experimental results indicate that the system can estimate gaze with an accuracy of 1.4-2.7? and is robust against large head movements. Two real-time human-computer interaction (HCI) applications are presented to demonstrate the potential of the proposed system for wide applications.
  @cite_21: 3D model-based gaze estimation methods are widely explored because of their good accuracy and ability to handle free head movement. Traditional methods with complex hardware systems (Eg. infrared lights, 3D sensors, etc.) are restricted to controlled environments, which significantly limit their practical utilities. In this paper, we propose a 3D model-based gaze estimation method with a single web-camera, which enables instant and portable eye gaze tracking. The key idea is to leverage on the proposed 3D eye-face model, from which we can estimate 3D eye gaze from observed 2D facial landmarks. The proposed system includes a 3D deformable eye-face model that is learned offline from multiple training subjects. Given the deformable model, individual 3D eye-face models and personal eye parameters can be recovered through the unified calibration algorithm. Experimental results show that the proposed method outperforms state-of-the-art methods while allowing convenient system setup and free head movement. A real time eye tracking system running at 30 FPS also validates the effectiveness and efficiency of the proposed method.
  @cite_1: Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06o, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.
  @cite_6: Most commercial eye gaze tracking systems are based on the use of infrared lights. However, such systems may not work outdoor or may have a very limited head box for them to work. This paper proposes a non-infrared based approach to track one's eye gaze with an RGBD camera (in our case, Kinect). The proposed method adopts a personalized 3D face model constructed off-line. To detect the eye gaze, our system tracks the iris center and a set of 2D facial landmarks whose 3D locations are provided by the RGBD camera. A simple onetime calibration procedure is used to obtain the parameters of the personalized eye gaze model. We compare the performance of the proposed method against the 2D approach using only RGB input on the same images, and find that the use of depth information directly from Kinect achieves more accurate tracking. As expected, the results from the proposed method are not as accurate as the ones from infrared-based approaches. However, this method has the potential for practical use with upcoming better and cheaper depth cameras.
  @cite_39: Low cost eye tracking is an actual challenging research topic for the eye tracking community. Gaze tracking based on a web cam and without infrared light is a searched goal to broaden the applications of eye tracking systems. Web cam based eye tracking results in new challenges to solve such as a wider field of view and a lower image quality. In addition, no infrared light implies that glints cannot be used anymore as a tracking feature. In this paper, a thorough study has been carried out to evaluate pupil (iris) center-eye corner (PC-EC) vector as feature for gaze estimation based on interpolation methods in low cost eye tracking, as it is considered to be partially equivalent to the pupil center-corneal reflection (PC-CR) vector. The analysis is carried out both based on simulated and real data. The experiments show that eye corner positions in the image move slightly when the user is looking at different points of the screen, even with a static head position. This lowers the possible accuracy of the gaze estimation, significantly reducing the accuracy of the system under standard working conditions to 2--3 degrees.
  @cite_0: We study gaze estimation on tablets; our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet are not constrained. We collected a large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. We made three major observations on the collected data and employed a baseline algorithm for analyzing the impact of several factors on gaze estimation accuracy. The baseline algorithm is based on multilevel HoG feature and Random Forests regressor, which achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various practical factors such as person dependency, dataset size, race, wearing glasses and user posture on the gaze estimation accuracy.
  @cite_10: Morphable face models are a powerful tool, but have previously failed to model the eye accurately due to complexities in its material and motion. We present a new multi-part model of the eye that includes a morphable model of the facial eye region, as well as an anatomy-based eyeball model. It is the first morphable model that accurately captures eye region shape, since it was built from high-quality head scans. It is also the first to allow independent eyeball movement, since we treat it as a separate part. To showcase our model we present a new method for illumination- and head-pose–invariant gaze estimation from a single RGB image. We fit our model to an image through analysis-by-synthesis, solving for eye region shape, texture, eyeball pose, and illumination simultaneously. The fitted eyeball pose parameters are then used to estimate gaze direction. Through evaluation on two standard datasets we show that our method generalizes to both webcam and high-quality camera images, and outperforms a state-of-the-art CNN method achieving a gaze estimation accuracy of (9.44^ ) in a challenging user-independent scenario.
summary: Traditional approaches to image-based gaze estimation are typically categorized as or . Feature-based approaches reduce an eye image down to a set of features based on hand-crafted rules @cite_39 @cite_1 @cite_26 @cite_0 and then feed these features into simple, often linear machine learning models to regress the final gaze estimate. Model-based methods instead attempt to fit a known 3D model to the eye image @cite_6 @cite_7 @cite_10 @cite_21 by minimizing a suitable energy.