{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:12:56Z","timestamp":1778605976445,"version":"3.51.4"},"reference-count":49,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2018,12,3]]},"DOI":"10.3233\/ica-180584","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T11:41:01Z","timestamp":1535715661000},"page":"85-95","source":"Crossref","is-referenced-by-count":89,"title":["DeepEye: Deep convolutional network for pupil detection in real environments"],"prefix":"10.1177","volume":"26","author":[{"given":"F.J.","family":"Vera-Olmos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E.","family":"Pardo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H.","family":"Melero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Malpica","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/ICA-180584_ref1","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1016\/j.eswa.2009.10.017","article-title":"EOG-based human-computer interface system development","volume":"37","author":"Deng","year":"2010","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/ICA-180584_ref2","unstructured":"Cannan J, Hu H. Human-machine interaction (HMI): A survey. University of Essex. 2011."},{"issue":"2","key":"10.3233\/ICA-180584_ref3","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/nrneurol.2012.273","article-title":"Eye movements in patients with neurodegenerative disorders","volume":"9","author":"Anderson","year":"2013","journal-title":"Nature Reviews Neurology"},{"issue":"1","key":"10.3233\/ICA-180584_ref4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s00406-014-0567-8","article-title":"Eye movements of patients with schizophrenia in a natural environment","volume":"266","author":"Dowiasch","year":"2016","journal-title":"European Archives of Psychiatry and Clinical Neuroscience"},{"issue":"6","key":"10.3233\/ICA-180584_ref5","first-page":"326","article-title":"Eye movement indices in the study of depressive disorder","volume":"28","author":"Li","year":"2016","journal-title":"Shanghai Archives of Psychiatry"},{"issue":"5","key":"10.3233\/ICA-180584_ref6","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1006\/rtim.2002.0279","article-title":"Real-time eye, gaze, and face pose tracking for monitoring driver vigilance","volume":"8","author":"Ji","year":"2002","journal-title":"Real-Time Imaging"},{"key":"10.3233\/ICA-180584_ref7","unstructured":"Wang Q, Yang J, Ren M, Zheng Y. Driver fatigue detection: A survey. In: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on IEEE. 2006; 2: 8587-8591."},{"key":"10.3233\/ICA-180584_ref8","first-page":"39","article-title":"Do adolescents attend to warnings in cigarette advertising? An eye-tracking approach","volume":"34","author":"Krugman","year":"1994","journal-title":"Journal of Advertising Research"},{"issue":"3","key":"10.3233\/ICA-180584_ref9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1080\/10447318.2013.847762","article-title":"The impact of advertising location and user task on the emergence of banner ad blindness: An eyetracking study","volume":"30","author":"Resnick","year":"2014","journal-title":"International Journal of Human-Computer Interaction"},{"issue":"6","key":"10.3233\/ICA-180584_ref10","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1093\/brain\/awh484","article-title":"Saccadic eye movement changes in Parkinson\u2019s disease dementia and dementia with Lewy bodies","volume":"128","author":"Mosimann","year":"2005","journal-title":"Brain"},{"key":"10.3233\/ICA-180584_ref11","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.2147\/NDT.S45931","article-title":"Saccadic eye movement applications for psychiatric disorders","volume":"9","author":"Bittencourt","year":"2013","journal-title":"Neuropsychiatric Disease and Treatment"},{"key":"10.3233\/ICA-180584_ref12","unstructured":"Goni S, Echeto J, Villanueva A, Cabeza R. Robust algorithm for pupil-glint vector detection in a video-oculography eyetracking system. In: Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on 4 IEEE. 2004; 4: 941-944."},{"key":"10.3233\/ICA-180584_ref13","doi-asserted-by":"crossref","unstructured":"Long X, Tonguz OK, Kiderman A. A high speed eye tracking system with robust pupil center estimation algorithm. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE. 2007; 3331-3334.","DOI":"10.1109\/IEMBS.2007.4353043"},{"key":"10.3233\/ICA-180584_ref14","doi-asserted-by":"crossref","unstructured":"Swirski L, Bulling A, Dodgson N. Robust real-time pupil tracking in highly off-axis images. In: Proceedings of the Symposium on Eye Tracking Research and Applications. ACM. 2012; 173-176.","DOI":"10.1145\/2168556.2168585"},{"issue":"9","key":"10.3233\/ICA-180584_ref15","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/TPAMI.2011.251","article-title":"Accurate eye center location through invariant isocentric patterns","volume":"34","author":"Valenti","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/ICA-180584_ref16","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1145\/633292.633452","article-title":"Trials and tribulations of using an eye-tracking system","author":"Schnipke","year":"2000","journal-title":"CHI\u201900 Extended Abstracts on Human Factors in Computing Systems. ACM"},{"key":"10.3233\/ICA-180584_ref17","doi-asserted-by":"crossref","unstructured":"Fuhl W, Santini TC, K\u00fcbler T, Kasneci E. Else: Ellipse selection for robust pupil detection in real-world environments. In: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. ACM. 2016; 123-130.","DOI":"10.1145\/2857491.2857505"},{"key":"10.3233\/ICA-180584_ref18","doi-asserted-by":"crossref","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv170605587. 2017.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.3233\/ICA-180584_ref19","doi-asserted-by":"crossref","unstructured":"Fuhl W, K\u00fcbler T, Sippel K, Rosenstiel W, Kasneci E. Excuse: Robust pupil detection in real-world scenarios. In: International Conference on Computer Analysis of Images and Patterns. Springer. 2015; 39-51.","DOI":"10.1007\/978-3-319-23192-1_4"},{"key":"10.3233\/ICA-180584_ref20","unstructured":"Keil A, Albuquerque G, Berger K, Magnor MA. Real-time gaze tracking with a consumer-grade video camera. 2010."},{"issue":"3","key":"10.3233\/ICA-180584_ref21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0169-2607(98)00105-9","article-title":"Robust pupil center detection using a curvature algorithm","volume":"59","author":"Zhu","year":"1999","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.3233\/ICA-180584_ref22","unstructured":"Li D, Winfield D, Parkhurst DJ. Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches. In: Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on IEEE. 2005. 79-79."},{"key":"10.3233\/ICA-180584_ref23","first-page":"8","article-title":"SET: a pupil detection method using sinusoidal approximation","author":"Javadi","year":"2015","journal-title":"Frontiers in Neuroengineering"},{"key":"10.3233\/ICA-180584_ref24","unstructured":"Fuhl W, Santini T, Kasneci G, Kasneci E. PupilNet: Convolutional neural networks for robust pupil detection. arXiv preprint arXiv160104902. 2016."},{"key":"10.3233\/ICA-180584_ref25","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/ICA-180584_ref26","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.3233\/ICA-180584_ref27","unstructured":"Badrinarayanan V, Kendall A, Cipolla R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv151100561. 2015."},{"key":"10.3233\/ICA-180584_ref28","doi-asserted-by":"crossref","unstructured":"Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: The IEEE International Conference on Computer Vision (ICCV). 2015.","DOI":"10.1109\/ICCV.2015.178"},{"key":"10.3233\/ICA-180584_ref29","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and ComputerAssisted Intervention. Springer. 2015; 234-241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.3233\/ICA-180584_ref30","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv preprint arXiv160600915. 2016."},{"key":"10.3233\/ICA-180584_ref31","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016; 779-788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.3233\/ICA-180584_ref32","doi-asserted-by":"crossref","unstructured":"Wang X, Shrivastava A, Gupta A. A-fast-rcnn: Hard positive generation via adversary for object detection. arXiv preprint arXiv170403414. 2017; 2.","DOI":"10.1109\/CVPR.2017.324"},{"key":"10.3233\/ICA-180584_ref33","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R. Mask r-cnn. In: Computer Vision (ICCV), 2017 IEEE International Conference on IEEE. 2017; 2980-2988.","DOI":"10.1109\/ICCV.2017.322"},{"issue":"12","key":"10.3233\/ICA-180584_ref34","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1111\/mice.12313","article-title":"Structural damage detection with automatic feature-extraction through deep learning","volume":"32","author":"Lin","year":"2017","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"issue":"10","key":"10.3233\/ICA-180584_ref35","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/mice.12297","article-title":"Automated pixel-level Pavement crack detection on 3D asphalt surfaces using a deep-learning network","volume":"32","author":"Zhang","year":"2017","journal-title":"Computer-Aided Civil and Infrastructure Engineering"},{"key":"10.3233\/ICA-180584_ref36","doi-asserted-by":"crossref","unstructured":"Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine. 2017.","DOI":"10.1016\/j.compbiomed.2017.09.017"},{"issue":"2","key":"10.3233\/ICA-180584_ref37","doi-asserted-by":"crossref","first-page":"04015066","DOI":"10.1061\/(ASCE)CO.1943-7862.0001047","article-title":"A novel machine learning model for estimation of sale prices of real estate units","volume":"142","author":"Rafiei","year":"2015","journal-title":"Journal of Construction Engineering and Management"},{"issue":"2","key":"10.3233\/ICA-180584_ref38","doi-asserted-by":"crossref","first-page":"e87470","DOI":"10.1371\/journal.pone.0087470","article-title":"Driving with binocular visual field loss? A study on a supervised on-road parcours with simultaneous eye and head tracking","volume":"9","author":"Kasneci","year":"2014","journal-title":"PloS One"},{"key":"10.3233\/ICA-180584_ref39","unstructured":"Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. 2015; 448-456."},{"key":"10.3233\/ICA-180584_ref40","unstructured":"Ioffe S. Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. arXiv preprint arXiv170203275. 2017."},{"key":"10.3233\/ICA-180584_ref41","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. 2012; 1097-1105."},{"key":"10.3233\/ICA-180584_ref42","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv14091556. 2014."},{"key":"10.3233\/ICA-180584_ref43","doi-asserted-by":"crossref","unstructured":"He K, Sun J. Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015; 5353-5360.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"10.3233\/ICA-180584_ref44","unstructured":"Srivastava RK, Greff K, Schmidhuber J. Highway networks. arXiv preprint arXiv150500387. 2015."},{"key":"10.3233\/ICA-180584_ref45","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1007\/978-3-642-75988-8_28","article-title":"A real-time algorithm for signal analysis with the help of the wavelet transform","author":"Holschneider","year":"1990","journal-title":"Wavelets. Springer"},{"key":"10.3233\/ICA-180584_ref46","doi-asserted-by":"crossref","unstructured":"Lazebnik S, Schmid C, Ponce J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on IEEE. 2006; 2: 2169-2178.","DOI":"10.1109\/CVPR.2006.68"},{"key":"10.3233\/ICA-180584_ref47","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. In: European Conference on Computer Vision. Springer. 2014; 346-361.","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"10.3233\/ICA-180584_ref48","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.patcog.2016.06.005","article-title":"Learning scale-variant and scaleinvariant features for deep image classification","volume":"61","author":"van Noord","year":"2017","journal-title":"Pattern Recognition"},{"key":"10.3233\/ICA-180584_ref49","unstructured":"Courbariaux M, Bengio Y, David JP. Binaryconnect: Training deep neural networks with binary weights during propagations. In: Advances in Neural Information Processing Systems. 2015; 3123-3131."}],"container-title":["Integrated Computer-Aided Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/ICA-180584","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:14:13Z","timestamp":1777454053000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/ICA-180584"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,3]]},"references-count":49,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/ica-180584","relation":{},"ISSN":["1069-2509","1875-8835"],"issn-type":[{"value":"1069-2509","type":"print"},{"value":"1875-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,3]]}}}