{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:59:36Z","timestamp":1780365576050,"version":"3.54.1"},"reference-count":83,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light Reflex measurement. To test the implemented solutions, we collected and publicly released PopEYE as one of the first datasets consisting of 15 k eye images belonging to 22 different subjects acquired through the aforementioned specialized ophthalmic device. Finally, we discuss the experimental results in terms of classification accuracy of the eye status, as well as computational load analysis, since the proposed solution is designed to be implemented in embedded boards, which have limited hardware resources in computational power and memory size.<\/jats:p>","DOI":"10.3390\/s23010386","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:19:46Z","timestamp":1672370386000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Vision-Based Eye Image Classification for Ophthalmic Measurement Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-3616","authenticated-orcid":false,"given":"Giovanni","family":"Gibertoni","sequence":"first","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, 41125 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2441-7524","authenticated-orcid":false,"given":"Guido","family":"Borghi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1743-3043","authenticated-orcid":false,"given":"Luigi","family":"Rovati","sequence":"additional","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, 41125 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.ifacol.2017.08.509","article-title":"Vision Based Measurement applied to Industrial Instrumentation","volume":"50","author":"Xavier","year":"2017","journal-title":"IFAC-PapersOnLine"},{"key":"ref_2","unstructured":"Shapiro, L.G., and Stockman, G.C. (2001). Computer Vision, Prentice Hall."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"381","DOI":"10.21275\/ART20203995","article-title":"Machine learning algorithms-a review","volume":"9","author":"Mahesh","year":"2020","journal-title":"Int. J. Sci. Res. (IJSR)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1146\/annurev.cs.03.060188.000323","article-title":"Fundamentals of expert systems","volume":"3","author":"Buchanan","year":"1988","journal-title":"Annu. Rev. Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.enbuild.2017.10.041","article-title":"The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings","volume":"158","author":"Gallagher","year":"2018","journal-title":"Energy Build."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Engelhardt, S., Oksuz, I., Zhu, D., Yuan, Y., Mukhopadhyay, A., Heller, N., Huang, S.X., Nguyen, H., Sznitman, R., and Xue, Y. (2021). Compound Figure Separation of Biomedical Images with Side Loss. Proceedings of the Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, Strasbourg, France, 1 October 2021, Springer International Publishing. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-88210-5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102048","DOI":"10.1016\/j.media.2021.102048","article-title":"Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking","volume":"71","author":"Zhao","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, S.J., Cho, K.J., and Oh, S. (2017). Development of machine learning models for diagnosis of glaucoma. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177726"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1016\/j.ophtha.2016.05.029","article-title":"Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier","volume":"123","author":"Asaoka","year":"2016","journal-title":"Ophthalmology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","article-title":"A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images","volume":"35","author":"Li","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal","volume":"316","year":"2016","journal-title":"JAMA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using","volume":"318","year":"2017","journal-title":"JAMA"},{"key":"ref_14","first-page":"264","article-title":"Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology","volume":"8","author":"Balyen","year":"2019","journal-title":"Asia-Pac. J. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/BF00578760","article-title":"On slit-lamp microscopy","volume":"39","author":"Schmidt","year":"1975","journal-title":"Doc. Ophthalmol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1111\/j.1365-2818.2012.03619.x","article-title":"Optical coherence tomography","volume":"247","author":"Podoleanu","year":"2012","journal-title":"J. Microsc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1136\/pgmj.75.883.282","article-title":"Funduscopy: A forgotten art?","volume":"75","author":"Roberts","year":"1999","journal-title":"Postgrad. Med. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1002\/wcs.1323","article-title":"Pupillometry","volume":"5","author":"Sirois","year":"2014","journal-title":"Wiley Interdiscip. Rev. Cogn. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.sjopt.2010.04.003","article-title":"Laser refractive surgery in glaucoma patients","volume":"25","author":"Osman","year":"2011","journal-title":"Saudi J. Ophthalmol."},{"key":"ref_20","first-page":"58","article-title":"The evolution of cataract surgery","volume":"113","author":"Davis","year":"2016","journal-title":"Mo. Med."},{"key":"ref_21","first-page":"52","article-title":"A simple Maxwellian optical system to investigate the photoreceptors contribution to pupillary light reflex","volume":"11941","author":"Gibertoni","year":"2022","journal-title":"Ophthalmic Technologies XXXII"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1097\/00055735-199512000-00004","article-title":"Pupillary light reflex","volume":"6","author":"Kardon","year":"1995","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lee, J., Stanley, M., Spanias, A., and Tepedelenlioglu, C. (2016, January 12\u201314). Integrating machine learning in embedded sensor systems for Internet-of-Things applications. Proceedings of the 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Limassol, Cyprus.","DOI":"10.1109\/ISSPIT.2016.7886051"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., and Torralba, A. (2016, January 27). Eye tracking for everyone. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.239"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16495","DOI":"10.1109\/ACCESS.2017.2735633","article-title":"A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms","volume":"5","author":"Kar","year":"2017","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Chennamma, H.R., and Yuan, X. (2013). A Survey on Eye-Gaze Tracking Techniques. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fedullo, T., Masetti, E., Gibertoni, G., Tramarin, F., and Rovati, L. (2022, January 16\u201319). On the Use of an Hyperspectral Imaging Vision Based Measurement System and Machine Learning for Iris Pigmentation Grading. Proceedings of the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada.","DOI":"10.1109\/I2MTC48687.2022.9806509"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Winkler, S., and Subramanian, R. (2013, January 3\u20135). Overview of Eye tracking Datasets. Proceedings of the 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX), Klagenfurt am W\u00f6rthersee, Austria.","DOI":"10.1109\/QoMEX.2013.6603239"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fischer, T., Chang, H.J., and Demiris, Y. (2018, January 8\u201314). Rt-gene: Real-time eye gaze estimation in natural environments. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_21"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Brousseau, B., Rose, J., and Eizenman, M. (2020). Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model. Sensors, 20.","DOI":"10.3390\/s20020543"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rahman, H., Ahmed, M.U., Barua, S., Funk, P., and Begum, S. (2021). Vision-Based Driver\u2019s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21238019"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Vajs, I., Kovi\u0107, V., Papi\u0107, T., Savi\u0107, A.M., and Jankovi\u0107, M.M. (2022). Spatiotemporal Eye-Tracking Feature Set for Improved Recognition of Dyslexic Reading Patterns in Children. Sensors, 22.","DOI":"10.3390\/s22134900"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Granka, L.A., Joachims, T., and Gay, G. (2004, January 25\u201329). Eye-tracking analysis of user behavior in WWW search. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK. SIGIR \u201904.","DOI":"10.1145\/1008992.1009079"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Agarwal, M., and Sivakumar, R. (2019, January 24\u201327). Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals. Proceedings of the 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2019.8919795"},{"key":"ref_35","unstructured":"Cech, J., and Soukupova, T. (2016). Real-time eye blink detection using facial landmarks. Cent. Mach. Perception, Dep. Cybern. Fac. Electr. Eng. Czech Tech. Univ. Prague, 1\u20138. Available online: https:\/\/vision.fe.uni-lj.si\/cvww2016\/proceedings\/papers\/05.pdf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cortacero, K., Fischer, T., and Demiris, Y. (2019, January 27\u201328). RT-BENE: A dataset and baselines for real-time blink estimation in natural environments. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00147"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fuhl, W., Kasneci, G., and Kasneci, E. (2021). TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types. arXiv.","DOI":"10.1109\/ISMAR52148.2021.00053"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tonsen, M., Zhang, X., Sugano, Y., and Bulling, A. (2016, January 14\u201317). Labelled pupils in the wild: A dataset for studying pupil detection in unconstrained environments. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, Charleston, SC, USA. ETRA \u201916.","DOI":"10.1145\/2857491.2857520"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104109","DOI":"10.1016\/j.imavis.2021.104109","article-title":"A survey of iris datasets","volume":"108","author":"Omelina","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_40","first-page":"125","article-title":"Accurate eye centre localisation by means of gradients","volume":"11","author":"Timm","year":"2011","journal-title":"Visapp"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"\u015awirski, L., Bulling, A., and Dodgson, N. (2012, January 28\u201330). Robust real-time pupil tracking in highly off-axis images. Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, CA, USA. ETRA \u201912.","DOI":"10.1145\/2168556.2168585"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.imavis.2014.10.007","article-title":"Eye detection using discriminatory Haar features and a new efficient SVM","volume":"33","author":"Chen","year":"2015","journal-title":"Image Vis. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bigun, J., and Smeraldi, F. (2001, January 6\u20138). Robust Face Detection Using the Hausdorff Distance. Proceedings of the Audio- and Video-Based Biometric Person Authentication, Halmstad, Sweden. Lecture Notes in Computer Science.","DOI":"10.1007\/3-540-45344-X"},{"key":"ref_44","unstructured":"Fuhl, W., Santini, T., Kasneci, G., and Kasneci, E. (2016). PupilNet: Convolutional Neural Networks for Robust Pupil Detection. arXiv."},{"key":"ref_45","first-page":"85","article-title":"DeepEye: Deep convolutional network for pupil detection in real environments","volume":"26","author":"Pardo","year":"2019","journal-title":"Integr. Comput.-Aided Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3196323","article-title":"Assessment of a Vision-Based Technique for an Automatic Van Herick Measurement System","volume":"71","author":"Fedullo","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khan, W., Hussain, A., Kuru, K., and Al-askar, H. (2020). Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision. Sensors, 20.","DOI":"10.3390\/s20133785"},{"key":"ref_48","unstructured":"(2022, November 30). Talking Face Video. Available online: https:\/\/personalpages.manchester.ac.uk\/staff\/timothy.f.cootes\/data\/talking_face\/talking_face.html."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2501643.2501647","article-title":"Hybrid method based on topography for robust detection of iris center and eye corners","volume":"9","author":"Villanueva","year":"2013","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kacete, A., Royan, J., Seguier, R., Collobert, M., and Soladie, C. (2016, January 7\u201310). Real-time eye pupil localization using Hough regression forest. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477666"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"21780","DOI":"10.1109\/JSEN.2022.3197235","article-title":"Pseudo RGB-D Face Recognition","volume":"22","author":"Jin","year":"2022","journal-title":"IEEE Sensors J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"15844","DOI":"10.1109\/ACCESS.2018.2810849","article-title":"Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process","volume":"6","author":"Zheng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/0042-6989(66)90078-2","article-title":"The maxwellian view","volume":"6","author":"Westheimer","year":"1966","journal-title":"Vis. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/LRA.2022.3167736","article-title":"Continual Learning in Real-Life Applications","volume":"7","author":"Graffieti","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_55","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Roth, S., and Schiele, B. (2008, January 23\u201328). People-tracking-by-detection and people-detection-by-tracking. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587583"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bolelli, F., Borghi, G., and Grana, C. (2017, January 11\u201315). Historical handwritten text images word spotting through sliding window HOG features. Proceedings of the International Conference on Image Analysis and Processing, Catania, Italy.","DOI":"10.1007\/978-3-319-68560-1_65"},{"key":"ref_59","unstructured":"Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S.Z. (2007, January 27\u201329). Learning multi-scale block local binary patterns for face recognition. Proceedings of the International Conference on Biometrics, Seoul, Republic of Korea."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_61","unstructured":"Riedmiller, M., and Lernen, A. (2014). Multi Layer Perceptron, Machine Learning Lab Special Lecture, University of Freiburg."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"Raschka, S. (2014). Naive bayes and text classification i-introduction and theory. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TAES.2007.357120","article-title":"Adaptive boosting for SAR automatic target recognition","volume":"43","author":"Sun","year":"2007","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_67","first-page":"1277","article-title":"Bayesian quadratic discriminant analysis","volume":"8","author":"Srivastava","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"10121","DOI":"10.1016\/j.jfranklin.2021.10.005","article-title":"Random radial basis function kernel-based support vector machine","volume":"358","author":"Ding","year":"2021","journal-title":"J. Frankl. Inst."},{"key":"ref_69","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_70","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/0165-1765(84)90126-5","article-title":"A note on the calculation and interpretation of the Gini index","volume":"15","author":"Lerman","year":"1984","journal-title":"Econ. Lett."},{"key":"ref_72","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_77","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_79","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv."},{"key":"ref_80","unstructured":"(2022, November 30). Available online: https:\/\/github.com\/keras-team\/keras."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13054-016-1239-z","article-title":"Reliability of standard pupillometry practice in neurocritical care: An observational, double-blinded study","volume":"20","author":"Couret","year":"2016","journal-title":"Crit. Care"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1001\/jamaophthalmol.2020.3466","article-title":"Utility of Pupillary Light Reflex Metrics as a Physiologic Biomarker for Adolescent Sport-Related Concussion","volume":"138","author":"Christina","year":"2020","journal-title":"JAMA Ophthalmol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Antoniou, E., Bozios, P., Christou, V., Tzimourta, K.D., Kalafatakis, K., Tsipouras, M.G., Giannakeas, N., and Tzallas, A.T. (2021). EEG-Based Eye Movement Recognition Using Brain\u2014Computer Interface and Random Forests. Sensors, 21.","DOI":"10.3390\/s21072339"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:55Z","timestamp":1760147755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010386"],"URL":"https:\/\/doi.org\/10.3390\/s23010386","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}