{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:35:02Z","timestamp":1776886502895,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T00:00:00Z","timestamp":1658361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903090"],"award-info":[{"award-number":["61903090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022GXNSFBA035644"],"award-info":[{"award-number":["2022GXNSFBA035644"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["QN2021033002L"],"award-info":[{"award-number":["QN2021033002L"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["61903090"],"award-info":[{"award-number":["61903090"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2022GXNSFBA035644"],"award-info":[{"award-number":["2022GXNSFBA035644"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["QN2021033002L"],"award-info":[{"award-number":["QN2021033002L"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foreign Young Talent Program","award":["61903090"],"award-info":[{"award-number":["61903090"]}]},{"name":"Foreign Young Talent Program","award":["2022GXNSFBA035644"],"award-info":[{"award-number":["2022GXNSFBA035644"]}]},{"name":"Foreign Young Talent Program","award":["QN2021033002L"],"award-info":[{"award-number":["QN2021033002L"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gaze estimation, which is a method to determine where a person is looking at given the person\u2019s full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with angular-error of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.<\/jats:p>","DOI":"10.3390\/s22145462","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T22:38:50Z","timestamp":1658443130000},"page":"5462","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Gaze Estimation Approach Using Deep Differential Residual Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Longzhao","family":"Huang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1213-3877","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Bouridane","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Environment, Northumbria University, Newcastle NE18ST, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3402-9576","authenticated-orcid":false,"given":"Ahmad","family":"Chaddad","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China"},{"name":"The Laboratory for Imagery Vision and Artificial Intelligence, Ecole de Technologie Superieure, 1100 Rue Notre Dame O, Montreal, QC H3C1K3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.dcn.2016.11.001","article-title":"Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?","volume":"25","author":"Eckstein","year":"2017","journal-title":"Dev. 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