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.
Recent studies aim to better represent the appearance, and @cite_28 proposed a low-dimensional feature extraction method. It divides the eye area into three columns and five columns and calculates the gray value and the percentage of each area. Therefore, a 15-dimensional feature vector is defined. However, this feature does not apply to eye images under free head movement. @cite_35 introduced a deep feature extracted from convolutional neural networks. The deep feature has sparse characters and provides a effective solution for gaze estimation.
@cite_28 @cite_35
2001252859 2492303557
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. 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.