Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
Because most existing gaze estimation datasets are designed for coarse gaze estimation, the sampling density of gaze and head pose space is not sufficient to train appearance-based gaze estimators @cite_16 @cite_22 @cite_2 @cite_26 (see Table for an overview of existing datasets). More comparable to , the Eyediap dataset contains 94 video sequences of 16 participants looking at three different targets (discrete and continuous markers displayed on a monitor, and floating physical targets) under both static and free head motion @cite_41 . The UT Multiview dataset also contains dense gaze samples of 50 participants as well as 3D reconstructions of eye regions that can be used to synthesise images for arbitrary head poses @cite_25 . However, as discussed before, both datasets have the significant limitation that they were recorded under controlled laboratory settings. Although the Eyediap dataset includes two different illumination conditions, recordings under the second condition were provided only for a subset of the participants.
@cite_26 @cite_22 @cite_41 @cite_2 @cite_16 @cite_25
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Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22 percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation. Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze that contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves the state of the art by 22 percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation. Within the past decade, many computational approaches have been developed to estimate gaze directions of persons based on their facial appearance. Most researchers used common face datasets with only a l imited representation of different head poses to train and verify their algorithms. Moreover, in most datasets, faces have neither a defined gaze direction, nor do they incor porate different combinations of eye gaze and head pose. Therefore, we recorded an extended dataset of 20 sub jects including faces in various combinations of head pos e and eye gaze leading to a total amount of 2220 colour i mages (111 per subject). The images were produced under controlled conditions, i.e. we used a technique to make sure that the subjects adjust their head and eyes appropriately. Furthermore, all images are manually labelled with landmarks indicating important featur es of the face. Finally, we evaluate the dataset with two computational methods for head pose and gaze estimation respectively. This paper presents a new, publicly available eye tracking dataset, aimed to be used as a benchmark for Point of Gaze (PoG) detection algorithms. The dataset consists of a set of videos recording the eye motion of human test subjects as they were looking at, or following, a set of predefined points of interest on a computer visual display unit. The eye motion was recorded using a Mobile Eye, head mounted, infrared monocular camera. The ground truth of the point of gaze and head location and direction in the three dimensional space are provided together with the data. The ground truth regarding the point of gaze at is known in advance since the subjects are always looking at predefined targets, whereas, the head position in 3D is captured using a Vicon Motion Tracking System. Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild. Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3 on MPIIGaze and 27.7 on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses. Free-head 3D gaze tracking outputs both the eye location and the gaze vector in 3D space, and it has wide applications in scenarios such as driver monitoring, advertisement analysis and surveillance. A reliable and low-cost monocular solution is critical for pervasive usage in these areas. Noticing that a gaze vector is a composition of head pose and eyeball movement in a geometrically deterministic way, we propose a novel gaze transform layer to connect separate head pose and eyeball movement models. The proposed decomposition does not suffer from head-gaze correlation overfitting and makes it possible to use datasets existing for other tasks. To add stronger supervision for better network training, we propose a two-step training strategy, which first trains sub-tasks with rough labels and then jointly trains with accurate gaze labels. To enable good cross-subject performance under various conditions, we collect a large dataset which has full coverage of head poses and eyeball movements, contains 200 subjects, and has diverse illumination conditions. Our deep solution achieves state-of-the-art gaze tracking accuracy, reaching 5.6° cross-subject prediction error using a small network running at 1000 fps on a single CPU (excluding face alignment time) and 4.3° cross-subject error with a deeper network. Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi-view gaze dataset and perform a 3D reconstruction in order to generate dense training data of eye images. By using the synthesized dataset to learn a random regression forest, we show that our method outperforms existing methods that use low-resolution eye images. Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild. In this paper, the recognition of the visual focus of attention (VFOA) of meeting participants (as defined by their eye gaze direction) from their head pose is addressed. To this end, the head pose observations are modeled using an hidden Markov model (HMM) whose hidden states corresponds to the VFOA. The novelties are threefold. First, contrary to previous studies on the topic, in our set-up, the potential VFOA of a person is not restricted to other participants only, but includes environmental targets (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan and tilt (as well) gaze space. Second, the HMM parameters are set by exploiting results from the cognitive science on saccadic eye motion, which allows to predict what the head pose should be given an actual gaze target. Third, an unsupervised parameter adaptation step is proposed which accounts for the specific gazing behaviour of each participant. Using a publicly available corpus of 8 meetings featuring 4 persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision based tracking system. T o infer human gaze from eye appearance, various methods have been proposed. However, most of them assume a fixed head pose because allowing free head motion adds 6 degrees of freedom to the problem and requires a prohibitively large number of training samples. In this paper, we aim at solving the appearance-based gaze estimation problem under free head motion without significantly increasing the cost of training. The idea is to decompose the problem into subproblems, including initial estimation under fixed head pose and subsequent compensations for estimation biases caused by head rotation and eye appearance distortion. Then each subproblem is solved by either learning-based method or geometric-based calculation. Specifically, the gaze estimation bias caused by eye appearance distortion is learnt effectively from a 5-seconds video clip. Extensive experiments were conducted to verify the effectiveness of the proposed approach. In this paper, we address the problem of free head motion in appearance-based gaze estimation. This problem remains challenging because head motion changes eye appearance significantly, and thus, training images captured for an original head pose cannot handle test images captured for other head poses. To overcome this difficulty, we propose a novel gaze estimation method that handles free head motion via eye image synthesis based on a single camera. Compared with conventional fixed head pose methods with original training images, our method only captures four additional eye images under four reference head poses, and then, precisely synthesizes new training images for other unseen head poses in estimation. To this end, we propose a single-directional (SD) flow model to efficiently handle eye image variations due to head motion. We show how to estimate SD flows for reference head poses first, and then use them to produce new SD flows for training image synthesis. Finally, with synthetic training images, joint optimization is applied that simultaneously solves an eye image alignment and a gaze estimation. Evaluation of the method was conducted through experiments to assess its performance and demonstrate its effectiveness. Existing appearance-based gaze estimation methods suffer from tedious calibration and appearance variation caused by head movement. In this paper, to handle this problem, we propose a novel appearance-based gaze estimation method by introducing supervised adaptive feature extraction and hierarchical mapping model. Firstly, an adaptive feature learning method is proposed to extract topology-preserving (TOP) feature individually. Then hierarchical mapping method is proposed to localize gaze position based on coarse-to-fine strategy. Appearance synthesis approach is used to increase the refer sample density. Experiments show that under the condition of sparse calibration, proposed method has better performance in accuracy than existing methods under fixed head pose without chinrest. Moreover, our method can be easily extended for head pose-varying gaze estimation. We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people of different ethnicities and a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, and make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution and vision aids as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers. As an indicator of attention, gaze is an important cue for human behavior and social interaction analysis. Recent deep learning methods for gaze estimation rely on plain regression of the gaze from images without accounting for potential mismatches in eye image cropping and normalization. This may impact the estimation of the implicit relation between visual cues and the gaze direction when dealing with low resolution images or when training with a limited amount of data. In this paper, we propose a deep multitask framework for gaze estimation, with the following contributions. (i) we proposed a multitask framework which relies on both synthetic data and real data for end-to-end training. During training, each dataset provides the label of only one task but the two tasks are combined in a constrained way. (ii) we introduce a Constrained Landmark-Gaze Model (CLGM) modeling the joint variation of eye landmark locations (including the iris center) and gaze directions. By relating explicitly visual information (landmarks) to the more abstract gaze values, we demonstrate that the estimator is more accurate and easier to learn. (iii) by decomposing our deep network into a network inferring jointly the parameters of the CLGM model and the scale and translation parameters of eye regions on one hand, and a CLGM based decoder deterministically inferring landmark positions and gaze from these parameters and head pose on the other hand, our framework decouples gaze estimation from irrelevant geometric variations in the eye image (scale, translation), resulting in a more robust model. Thorough experiments on public datasets demonstrate that our method achieves competitive results, improving over state-of-the-art results in challenging free head pose gaze estimation tasks and on eye landmark localization (iris location) ones. A gaze concurrence is a point in 3D where the gaze directions of two or more people intersect. It is a strong indicator of social saliency because the attention of the participating group is focused on that point. In scenes occupied by large groups of people, multiple concurrences may occur and transition over time. In this paper, we present a method to construct a 3D social saliency field and locate multiple gaze concurrences that occur in a social scene from videos taken by head-mounted cameras. We model the gaze as a cone-shaped distribution emanating from the center of the eyes, capturing the variation of eye-in-head motion. We calibrate the parameters of this distribution by exploiting the fixed relationship between the primary gaze ray and the head-mounted camera pose. The resulting gaze model enables us to build a social saliency field in 3D. We estimate the number and 3D locations of the gaze concurrences via provably convergent mode-seeking in the social saliency field. Our algorithm is applied to reconstruct multiple gaze concurrences in several real world scenes and evaluated quantitatively against motion-captured ground truth. 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. Abstract—Automated estimation of the allocation of adriver’s visual attention may be a critical component offuture Advanced Driver Assistance Systems. In theory,vision-based tracking of the eye can provide a good estimateof gaze location. In practice, eye tracking from videois challenging because of sunglasses, eyeglass reﬂections,lighting conditions, occlusions, motion blur, and otherfactors. Estimation of head pose, on the other hand, isrobust to many of these effects, but cannot provide as ﬁne-grained of a resolution in localizing the gaze. However,for the purpose of keeping the driver safe, it is sufﬁcientto partition gaze into regions. In this effort, we proposea system that extracts facial features and classiﬁes theirspatial conﬁguration into six regions in real-time. Ourproposed method achieves an average accuracy of 91.4 at an average decision rate of 11 Hz on a dataset of 50drivers from an on-road study.Index Terms—Head pose estimation, gaze tracking, driverdistraction, driver assistance systems, on-road study. We investigate the appearance-based gaze estimation problem, with respect to its essential difficulty in reducing the number of required training samples, and other practical issues such as slight head motion, image resolution variation, and eye blinking. We cast the problem as mapping high-dimensional eye image features to low-dimensional gaze positions, and propose an adaptive linear regression (ALR) method as the key to our solution. The ALR method adaptively selects an optimal set of sparsest training samples for the gaze estimation via l 1 -optimization. In this sense, the number of required training samples is significantly reduced for high accuracy estimation. In addition, by adopting the basic ALR objective function, we integrate the gaze estimation, subpixel alignment and blink detection into a unified optimization framework. By solving these problems simultaneously, we successfully handle slight head motion, image resolution variation and eye blinking in appearance-based gaze estimation. We evaluated the proposed method by conducting experiments with multiple users and variant conditions to verify its effectiveness. Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learning-by-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, appearance variability, and the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses real-time approximations for complex eyeball materials and structures as well as anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework available online for the benefit of the research community.