ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation

Zhang, Xucong; Park, Seonwook; Beeler, Thabo; Bradley, Derek; Tang, Siyu; Hilliges, Otmar

doi:10.1007/978-3-030-58558-7_22

Cited by 169 publications

(162 citation statements)

References 39 publications

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“…Therefore, similar to the computer vision tasks [81], the deeper CNN architecture usually achieves better performance. A number of CNN architectures, which have been proposed for typical computer vision tasks, also show great success in gaze estimation task, e.g., LeNet [26], AlexNet [43], VGG [42], ResNet18 [36] and ResNet50 [82]. Besides, some well-designed modules also help to improve the estimation accuracy [46], [49], [83], [84] , e.g., Chen et al propose to use dilated convolution to extract features from eye images [46], Cheng et al propose an attention module for fusing two eye features [49].…”

Section: B Cnn Modelsmentioning

confidence: 99%

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Cheng,

Wang,

Bao

et al. 2021

Preprint

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Gaze estimation reveals where a person is looking. It is an important clue for understanding human intention. The recent development of deep learning has revolutionized many computer vision tasks, the appearance-based gaze estimation is no exception. However, it lacks a guideline for designing deep learning algorithms for gaze estimation tasks. In this paper, we present a comprehensive review of the appearance-based gaze estimation methods with deep learning. We summarize the processing pipeline and discuss these methods from four perspectives: deep feature extraction, deep neural network architecture design, personal calibration as well as device and platform. Since the data pre-processing and post-processing methods are crucial for gaze estimation, we also survey face/eye detection method, data rectification method, 2D/3D gaze conversion method, and gaze origin conversion method. To fairly compare the performance of various gaze estimation approaches, we characterize all the publicly available gaze estimation datasets and collect the code of typical gaze estimation algorithms. We implement these codes and set up a benchmark of converting the results of different methods into the same evaluation metrics. This paper not only serves as a reference to develop deep learning-based gaze estimation methods but also a guideline for future gaze estimation research. Implemented methods and data processing codes are available at http://phi-ai.org/GazeHub.

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Section: B Cnn Modelsmentioning

confidence: 99%

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Cheng,

Wang,

Bao

et al. 2021

Preprint

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“…Deep learning is a powerful technique that has developed fast and is widely used in many applications [25,26] including computer vision [27] and gaze estimation [28,29]. Furthermore, there have been several open-source datasets for gaze estimation from the research community, for instance, MPIIGaze [22], GazeCapture [30], TabletGaze [31], including head pose and gaze database [32], ETH-XGaze [33], and RT-GENE [34]. Several approaches have been proposed for appearance-based gaze estimation.…”

Section: Related Workmentioning

confidence: 99%

Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network

2021

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Gaze estimation plays a significant role in understating human behavior and in human–computer interaction. Currently, there are many methods accessible for gaze estimation. However, most approaches need additional hardware for data acquisition which adds an extra cost to gaze tracking. The classic gaze tracking approaches usually require systematic prior knowledge or expertise for practical operations. Moreover, they are fundamentally based on the characteristics of the eye region, utilizing infrared light and iris glint to track the gaze point. It requires high-quality images with particular environmental conditions and another light source. Recent studies on appearance-based gaze estimation have demonstrated the capability of neural networks, especially convolutional neural networks (CNN), to decode gaze information present in eye images and achieved significantly simplified gaze estimation. In this paper, a gaze estimation method that utilizes a CNN for gaze estimation that can be applied to various platforms without additional hardware is presented. An easy and fast data collection method is used for collecting face and eyes images from an unmodified desktop camera. The proposed method registered good results; it proves that it is possible to predict the gaze with reasonable accuracy without any additional tools.

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“…However, to our knowledge, there is currently no low-resolution database with these characteristics, emphasizing how expensive it would be to create a similar database. The lack of databases for gaze-estimation is a well-known problem, although more and more efforts are being made to create better quality large scale databases [ 29 , 30 , 31 , 32 ].…”

Section: Working Frameworkmentioning

confidence: 99%

Low-Cost Eye Tracking Calibration: A Knowledge-Based Study

Garde

Larumbe-Bergera

Bossavit

et al. 2021

Sensors

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Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7∘, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.

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ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation

Cited by 169 publications

References 39 publications

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network

Low-Cost Eye Tracking Calibration: A Knowledge-Based Study

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