Appearance-based gaze estimation using deep features and random forest regression

Wang, Yafei; Shen, Tianyi; Yuan, Guoliang; Bian, Jiming; Fu, Xianping

doi:10.1016/j.knosys.2016.07.038

Cited by 54 publications

(21 citation statements)

References 9 publications

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“…Different appearance-based gaze estimation studies have been designed for 2D [11,18], 3D gaze estimation [18,32,33], classification and regression. Earlier studies focused on Artificial Neural Networks (ANNs) [34,35], random forest [21,33], linear regression [36], support vector regression (SVRs) [19], multimodal models [11,32], deep end-to-end CNN models [3,10], incremental learning [37] and transfer learning [38]. Some studies introduced hybrid appearancebased techniques.…”

Section: Survey Of Gaze Estimation Methodsmentioning

confidence: 99%

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Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

Akinyelu

Blignaut

2020

IEEE Access

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Eye tracking is becoming a very important tool across many domains, including humancomputer-interaction, psychology, computer vision, and medical diagnosis. Different methods have been used to tackle eye tracking, however, some of them are inaccurate under real-world conditions, while some require explicit user calibration which can be burdensome. Some of these methods suffer from poor image quality and variable light conditions. The recent success and prevalence of deep learning have greatly improved the performance of eye-tracking. The availability of large-scale datasets has further improved the performance of deep learning-based methods. This paper presents a survey of the current state-of-the-art on deep learning-based gaze estimation techniques, with a focus on Convolutional Neural Networks (CNN). This paper also provides a survey on other machine learning-based gaze estimation techniques. This study aims to empower the research community with valuable and useful insights that can enhance the design and development of improved and efficient deep learning-based eye-tracking models. This study also provides information on various pre-trained models, network architectures, and open-source datasets that are useful for training deep learning models.

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Section: Survey Of Gaze Estimation Methodsmentioning

confidence: 99%

“…In the literature, gaze tracking accuracy metrics have been reported in different ways, including angular accuracy in degrees [1,14,21], distance accuracy in cm/mm [10,22], and gaze estimation accuracy in percentage [3,13,23].…”

Section: ) Performance Metrics For Gaze Estimationmentioning

confidence: 99%

Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

Akinyelu

Blignaut

2020

IEEE Access

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“…However, this feature does not apply to eye images under free head movement. Wang et al [31] introduced a deep feature extracted from convolutional neural networks. The deep feature has sparse characters and provides a effective solution for gaze estimation.…”

Section: Appearance-based Gaze Estimationmentioning

confidence: 99%

Purifying naturalistic images through a real-time style transfer semantics network

Zhao

Yan

Shehu

et al. 2019

Engineering Applications of Artificial Intelligence

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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 (eg, gaze direction, pupil center position) of an input image (real image) while inferring style information (eg, 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 * Both Xianping Fu and Huibing Wang are corresponding authors.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.

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“…Zhang et al [22] first extracted three low-dimensional features from the eye images, including the color opponency, gray scale intensities and direction information, and then used a KNN classifier with k = 13 to learn the mapping from image features to gaze direction. Wang et al [23] added the depth feature to the traditional gaze estimation and applied the RF regression based on cluster-to-classify node splitting rules. Kacete et al [24] used RF regression to estimate the gaze vector from the high dimensional data with the face information.…”

Section: Introductionmentioning

confidence: 99%

Learning a 3D Gaze Estimator With Adaptive Weighted Strategy

et al. 2020

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As a method of predicting the target's attention distribution, gaze estimation plays an important role in human-computer interaction. In this paper, we learn a 3D gaze estimator with adaptive weighted strategy to get the mapping from the complete images to the gaze vector. We select the both eyes, the complete face and their fusion features as the input of the regression model of gaze estimator. Considering that the different areas of the face have different contributions on the results of gaze estimation under free head movement, we design a new learning strategy for the regression net. To improve the efficiency of the regression model to a great extent, we propose a weighted network that can adjust the learning strategy of the regression net adaptively. Experimental results conducted on the MPIIGaze and EyeDiap datasets demonstrate that our method can achieve superior performance compared with other state-of-the-art 3D gaze estimation methods.

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Appearance-based gaze estimation using deep features and random forest regression

Cited by 54 publications

References 9 publications

Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

Convolutional Neural Network-Based Methods for Eye Gaze Estimation: A Survey

Purifying naturalistic images through a real-time style transfer semantics network

Learning a 3D Gaze Estimator With Adaptive Weighted Strategy

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