CI-Net: Appearance-Based Gaze Estimation via Cooperative Network

Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency...

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Main Authors: Yuan Luo, Jiangtao Chen, Jian Chen
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9841577/
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author Yuan Luo
Jiangtao Chen
Jian Chen
author_facet Yuan Luo
Jiangtao Chen
Jian Chen
author_sort Yuan Luo
collection DOAJ
description Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency estimation network (I-Net). C-Net is used to estimate the Main gaze of the true gaze, and an attention mechanism is added to adaptively assign the weight between eyes and face features. The I-Net is used to estimate the Residual residuals based on true gaze. In addition, Cross attention module is designed in this paper, through which I-Net can selectively obtain information from C-Net, to obtain more accurate eyes directions. The experimental results in this paper show that the CI-Net gain lower angle errors than the current mainstream CNN methods under the condition of different appearance of both eyes and facial occlusion.
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spelling doaj.art-320246339a9a4e98999ed672952165482022-12-22T02:34:02ZengIEEEIEEE Access2169-35362022-01-0110787397874610.1109/ACCESS.2022.31941239841577CI-Net: Appearance-Based Gaze Estimation via Cooperative NetworkYuan Luo0https://orcid.org/0000-0002-1405-817XJiangtao Chen1https://orcid.org/0000-0001-5048-3336Jian Chen2https://orcid.org/0000-0002-9512-4123Key Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChianKey Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChianKey Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChianFacial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency estimation network (I-Net). C-Net is used to estimate the Main gaze of the true gaze, and an attention mechanism is added to adaptively assign the weight between eyes and face features. The I-Net is used to estimate the Residual residuals based on true gaze. In addition, Cross attention module is designed in this paper, through which I-Net can selectively obtain information from C-Net, to obtain more accurate eyes directions. The experimental results in this paper show that the CI-Net gain lower angle errors than the current mainstream CNN methods under the condition of different appearance of both eyes and facial occlusion.https://ieeexplore.ieee.org/document/9841577/Gaze estimationdeep learningmain gazeresidual residuals
spellingShingle Yuan Luo
Jiangtao Chen
Jian Chen
CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
IEEE Access
Gaze estimation
deep learning
main gaze
residual residuals
title CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
title_full CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
title_fullStr CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
title_full_unstemmed CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
title_short CI-Net: Appearance-Based Gaze Estimation via Cooperative Network
title_sort ci net appearance based gaze estimation via cooperative network
topic Gaze estimation
deep learning
main gaze
residual residuals
url https://ieeexplore.ieee.org/document/9841577/
work_keys_str_mv AT yuanluo cinetappearancebasedgazeestimationviacooperativenetwork
AT jiangtaochen cinetappearancebasedgazeestimationviacooperativenetwork
AT jianchen cinetappearancebasedgazeestimationviacooperativenetwork