Adaptive Linear Regression for Appearance-Based Gaze Estimation

Abstract:With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.

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“…This is based on mathematical principles, and can be categorized as descriptive statistics and inferential statistics [5][6][7].…”

Section: Discussionmentioning

confidence: 99%

“…(2) Data cleaning Planting area x 4 Air temperature x 5 Cost of production x 6 Market trading price Y Total harvest (…”

Section: ) Data Integrationmentioning

confidence: 99%

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

et al. 2016

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“…Recent studies aim to better represent the appearance, and Lu et al [30] 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.…”

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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“…Williams et al [3] build a sparse and semisupervised Gaussian process regression model which uses a mixture of different image features to improve accuracy and consistency of gaze estimation. Lu et al [4] propose an adaptive linear regression method to select an optimal set of sparest training samples for gaze estimation, thus less training samples are required. Sugano et al [5] propose a multi-camera system to reconstruct the 3D shape of eye regions and then use a random regression forest for person and head pose independent gaze estimation.…”

Section: Introductionmentioning

confidence: 99%

Gaze estimation driven solution for interacting children with ASD

Cai

Zhou

et al. 2015

2015 International Symposium on Micro-NanoMechatronics and Human Science (MHS)

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This paper investigates gaze estimation solutions for interacting children with Autism Spectrum Disorders (ASD). Previous research shows that satisfactory accuracy of gaze estimation can be achieved in constrained settings. However, most of the existing methods can not deal with large head movement (LHM) that frequently happens when interacting with children with ASD scenarios. We propose a gaze estimation method aiming at dealing with large head movement and achieving real time performance. An intervention table equipped with multiple sensors is designed to capture images with LHM. Firstly, reliable facial features and head poses are tracked using supervised decent method. Secondly, a convolution based integer-differential eye localization approach is used to locate the eye center efficiently and accurately. Thirdly, a rotation invariant gaze estimation model is built based on the located facial features, eye center, head pose and the depth data captured from Kinect. Finally, a multi-sensor fusion strategy is proposed to adaptively select the optimal camera to estimate the gaze as well as to fuse the depth information of the Kinect with the web camera. Experimental results showed that the gaze estimation method can achieve acceptable accuracy even in LHM situation and could potentially be applied in therapy for children with ASD.

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Adaptive Linear Regression for Appearance-Based Gaze Estimation

Cited by 208 publications

References 38 publications

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

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

Gaze estimation driven solution for interacting children with ASD

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