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.
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.
@cite_26 @cite_7 @cite_21 @cite_1 @cite_6 @cite_39 @cite_0 @cite_10
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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. 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. We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image gaze pairs) and infer their gaze accordingly. In this context, the paper makes three contributions: (i) unlike most previous approaches, we exploit the coupling (and constraints) between both eyes to infer their gaze jointly; (ii) we show that a generic gaze appearance model built from the aggregation of person-specific models can be used to handle unseen users and compensate for appearance variations across people, since a test user eyes' appearance will be reconstructed from similar users within the generic model. (iii) we propose an automatic model selection method that leads to comparable performance with a reduced computational load. 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.