Abstract Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the realism of the synthetic image. Different from the previous methods, this paper takes the first step to purify real images. Through the style transfer task, the distribution of outdoor real images is converted into indoor synthetic images, thereby reducing the influence of light. Therefore, this paper proposes a real-time style transfer network that preserves image content information (e.g., gaze direction, pupil center position) of an input image (real image) while inferring style information (e.g., image color structure, semantic features) of style image (synthetic image). In addition, the network accelerates the convergence speed of the model and adapts to multi-scale images. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. Qualitatively, it compares the proposed method with the available methods in a series of indoor and outdoor scenarios of the LPW dataset. In quantitative terms, it evaluates the purified image by training a gaze estimation model on the cross data set. The results show a significant improvement over the baseline method compared to the raw real image.
In general, there are two main types of eye gaze estimation methods: feature-based and appearance-based @cite_15 . Feature-based methods are intended to identify local features of the eye, such as contours, corners of the eye, and reflections from images of the eye. Pupil and corneal reflexes are commonly used for eye localization. Calibration with high-resolution cameras and other specialized hardware such as synchronized cameras and light sources can extract more precise geometric properties. However, the gaze feature is not stable enough under natural light.
@cite_15
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Conventional appearance-based gaze estimation methods employ local or global features as eye gaze appearance descriptor. But these methods don't work well under natural light with free head movement. To solve this problem, we present an appearance-based gaze estimation method using deep feature representation and feature forest regression. The deep feature is learned through hierarchical extraction of deep Convolutional Neural Network (CNN). And random forest regression with cluster-to-classify node splitting rules is used to take advantage of data distribution in sparse feature space. Experimental results demonstrate that the deep feature has a better performance than local features on calibrated gaze regression. The combination of deep features and random forest regression provides an effective solution for gaze estimation in a natural environment. 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. 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. 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.