Gaze Estimation Based on Deep Learning Method

Xia, Yifan; Liang, Baosheng

doi:10.1145/3424978.3425003

Cited by 4 publications

(7 citation statements)

References 16 publications

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“…The mobile device is another common platform for gaze estimation [35], [50], [124]. Such devices often contain front cameras, but the computational resources are limited.…”

Section: Devices and Platformsmentioning

confidence: 99%

“…Eye image [26] - [42], [119], [113] [38], [47], [48], [95], [120] [40], [46], [51], [98], [100], [115], [121] [49], [50], [79], [52], [53], [80], [54] -Facial image - [35] [43], [64], [101] [66], [68], [104], [108] [5], [46], [69], [92], [99], [110], [111], [116], [118], [122] [49], [50], [67], [59], [61], [83], [62], [123], [78], [124], [63], [112], [82], [37], [107] [60]…”

Section: Featurementioning

confidence: 99%

“…[43], [64], [42], [101], [113], [119] [38], [47], [66], [68], [95], [104], [108], [120] [5], [36], [40], [46], [70], [71], [72], [98], [99], [100], [115], [116], [118], [121], [122] [82], [50], [49], [37], [59], [61], [83], [62], [123], [78], [79], [124], [52], [53], [80], [63], [125], [107] -Semi-/Self-/Un-Supervised CNN --- [48] [51], [69], [92], [110], [111] [54], [112], [37...…”

Section: Modelmentioning

confidence: 99%

“…Single camera [26] [35] [43], [64], [42], [113] [38], [47], [48], [66], [68], [95], [104], [108], [120] [5], [36], [40], [46], [69], [70], [71], [72], [100], [111], [115], [116], [118], [121], [122] [59], [49], [50], [61], [83], [62], [123], [78], [79], [124], [52], [53], [80], [63], [125], [82], [37], [107] [60]…”

Section: Cameramentioning

confidence: 99%

See 3 more Smart Citations

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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“…The mobile device is another common platform for gaze estimation [35], [50], [124]. Such devices often contain front cameras, but the computational resources are limited.…”

Section: Devices and Platformsmentioning

confidence: 99%

Section: Featurementioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Cameramentioning

confidence: 99%

See 2 more Smart Citations

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

Cheng,

Wang,

Bao

et al. 2021

Preprint

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“…In recent years, appearance-based gaze estimation methods have become more popular due to increased processing power and improvements in computer vision techniques. Recent research studies have shown good progress, especially in using convolutional neural networks [ 49 , 50 , 51 ] and deep learning models [ 52 , 53 ] to estimate the user’s gaze with high accuracy.…”

Section: Appendix A1 Appearance-based Gaze Estimation Methodsmentioning

confidence: 99%

High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods

Narcizo

Santos

Hansen

2021

Vision

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This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range.

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