Relevant bibliographies by topics / Eye detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Eye detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Eye detection"

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Patil, Vaibhavi, Sakshi Patil, Krishna Ganjegi, and Pallavi Chandratre. "Face and Eye Detection for Interpreting Malpractices in Examination Hall." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1119–23. http://dx.doi.org/10.22214/ijraset.2022.41456.

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Abstract: One of the most difficult problems in computer vision is detecting faces and eyes. The purpose of this work is to give a review of the available literature on face and eye detection, as well as assessment of gaze. With the growing popularity of systems based on face and eye detection in a range of disciplines in recent years, academia and industry have paid close attention to this topic. Face and eye identification has been the subject of numerous investigations. Face and eye detection systems have made significant process despite numerous challenges such as varying illumination cond
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S.Priyadharsini. "Prevention from Road Accidents by Detecting Driver Drowsiness." Recent Trends in Information Technology and its Application 5, no. 2 (2022): 1–13. https://doi.org/10.5281/zenodo.6789736.

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Driver lethargy is one of the main explanations for traffic accidents and the associated fiscal losses. Existing drowsiness detection techniques does not concentrate on all the key factors of drowsy drivers. The proposed system designed for the analysis and detection of drowsiness uses visual based features. The eye state, eye blinking frequency, eye closure duration, redness level detection, mouth state, yawning frequency are the key factors for detecting drowsiness. Systems that use this technique usually monitor eye states and the position of the iris through a specific time period to estim
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Journal, IJSREM. "Eye Disease Detection Portal." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28164.

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Eye diseases and cancer, affecting millions of people in the developing world, can lead to vision loss. Tomography, a type of X-ray technique, is used for their detection, but symptoms like pain, blurriness, and redness may go unnoticed. Limited access to expertise in metropolitan areas poses a challenge for accurate diagnosis, despite the availability of scanning centers in many towns. By utilizing Deep Learning techniques, we have revolutionized the detection of eye diseases and cancer. This project involves extensive datasets obtained from previously scanned tomography scans, which are subj
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Mohammed, Anes J., and Dr.A.R.JayaSudha. "Driver Drowsiness Detection System." Advanced Innovations in Computer Programming Languages 5, no. 2 (2023): 8–15. https://doi.org/10.5281/zenodo.8037360.

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<em>Drowsy drivers cause several accidents every year. It&#39;s a major contributor to vehicular mishaps in the modern era. According to recent data, driver fatigue is a leading cause of accidents. Thousands of people lose their lives every year in vehicle accidents brought on by sleepy drivers. Drowsiness contributes to almost 30% of all accidents. A system that can detect driver fatigue and provide an alarm in time to avert an accident is essential. In this study, we provide a method for identifying sleepy drivers. In this system, the driver is constantly watched over by a camera. The driver
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B N, Ramya, MSJ Navaneeth Charan, Vishruth S, Suman R, and Thanmayi B. "Human Eye Disease Detection System Using Deep Learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 16212–17. https://doi.org/10.55248/gengpi.6.0525.19100.

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Fogelton, Andrej, and Wanda Benesova. "Eye blink completeness detection." Computer Vision and Image Understanding 176-177 (November 2018): 78–85. http://dx.doi.org/10.1016/j.cviu.2018.09.006.

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Hilal, Pranali Pandurang. "Eye Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2610–13. http://dx.doi.org/10.22214/ijraset.2024.59188.

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Abstract: Nowadays a lot of people have eye disease problems and to know their disease they do have to wait a lot because of the machine system in the hospital. To resolve that issue, we have developed an eye disease detection model using machine learning technology which will help the patient to know their disease as early as they can. The eye disease detection model is trained on a huge number of parameters so that can predict eye disease quickly
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R., Manikandan, Abilash S., Agilakalanchian C., and Tamilselvan P. "DRIVER DROWSINESS DETECTION SYSTEM USING OPEN COMPUTER VISION." International Journal of Current Research and Modern Education 3, no. 1 (2018): 410–14. https://doi.org/10.5281/zenodo.1218681.

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In recent years driver fatigue is one of the major causes of vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver i.e. drowsiness.&nbsp; So it is very important to detect the drowsiness of the driver to save life and property. This project is aimed towards developing a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness
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Patel, Mitesh, Sara Lal, Diarmuid Kavanagh, and Peter Rossiter. "Fatigue Detection Using Computer Vision." International Journal of Electronics and Telecommunications 56, no. 4 (2010): 457–61. http://dx.doi.org/10.2478/v10177-010-0062-8.

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Fatigue Detection Using Computer VisionLong duration driving is a significant cause of fatigue related accidents of cars, airplanes, trains and other means of transport. This paper presents a design of a detection system which can be used to detect fatigue in drivers. The system is based on computer vision with main focus on eye blink rate. We propose an algorithm for eye detection that is conducted through a process of extracting the face image from the video image followed by evaluating the eye region and then eventually detecting the iris of the eye using the binary image. The advantage of
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Nadella, Bhargavi. "Eye Detection and Tracking and Eye Gaze Estimation." Asia-pacific Journal of Convergent Research Interchange 1, no. 2 (2015): 25–42. http://dx.doi.org/10.21742/apjcri.2015.06.04.

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Dissertations / Theses on the topic "Eye detection"

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Hossain, Akdas, and Emma Miléus. "Eye Movement Event Detection for Wearable Eye Trackers." Thesis, Linköpings universitet, Matematik och tillämpad matematik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129616.

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Eye tracking research is a growing area and the fields as where eye trackingcould be used in research are large. To understand the eye tracking data dif-ferent filters are used to classify the measured eye movements. To get accu-rate classification this thesis has investigated the possibility to measure bothhead movements and eye movements in order to improve the estimated gazepoint.The thesis investigates the difference in using head movement compensationwith a velocity based filter, I-VT filter, to using the same filter without headmovement compensation. Further on different velocity thresho
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Trejo, Guerrero Sandra. "Model-Based Eye Detection and Animation." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7059.

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<p>In this thesis we present a system to extract the eye motion from a video stream containing a human face and applying this eye motion into a virtual character. By the notation eye motion estimation, we mean the information which describes the location of the eyes in each frame of the video stream. Applying this eye motion estimation into a virtual character, we achieve that the virtual face moves the eyes in the same way than the human face, synthesizing eye motion into a virtual character. In this study, a system capable of face tracking, eye detection and extraction, and finally iris posi
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Miao, Yufan. "Landmark Detection for Mobile Eye Tracking." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301499.

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Mobile eye tracking studies in urban environments can provide important insights into several processes of human behavior, ranging from wayfinding to human-environment interaction. The analysis of this kind of eye tracking data are based on a semi-manual or even sometimes completely manual process, consuming immense post-processing time. In this thesis, we propose an approach based on computer vision methods that allows fully automatic analysis of eye tracking data, captured in an urban environment. We present our approach, as well as the results of three experiments that were conducted in ord
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Bandara, Indrachapa Buwaneka. "Driver drowsiness detection based on eye blink." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9782/.

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Accidents caused by drivers’ drowsiness behind the steering wheel have a high fatality rate because of the discernible decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedback to maintain maximum performance. The main objective of this research study is to develop a reliable metric and system for the detection of driver impairment due
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Yi, Fei. "Robust eye coding mechanisms in humans during face detection." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/31011/.

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We can detect faces more rapidly and efficiently compared to non-face object categories (Bell et al., 2008; Crouzet, 2011), even when only partial information is visible (Tang et al., 2014). Face inversion impairs our ability to recognise faces. The key to understand this effect is to determine what special face features are processed and how coding of these features is affected by face inversion. Previous studies from our lab showed coding of the contralateral eye in an upright face detection task, which was maximal around the N170 recorded at posterior-lateral electrodes (Ince et al., 2016b;
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Anderson, Travis M. "Motion detection algorithm based on the common housefly eye." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1400965531&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Samadzadegan, Sepideh. "Automatic and Adaptive Red Eye Detection and Removal : Investigation and Implementation." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77977.

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Redeye artifact is the most prevalent problem in the flash photography, especially using compact cameras with built-in flash, which bothers both amateur and professional photographers. Hence, removing the affected redeye pixels has become an important skill. This thesis work presents a completely automatic approach for the purpose of redeye detection and removal and it consists of two modules: detection and correction of the redeye pixels in an individual eye, detection of two red eyes in an individual face.This approach is considered as a combination of some of the previous attempts in the ar
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Vidal, Diego Armando Benavides. "A Kernel matching approach for eye detection in surveillance images." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/24112.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2016.<br>Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2017-06-27T13:16:54Z No. of bitstreams: 1 2016_DiegoArmandoBenavidesVidal.pdf: 6256311 bytes, checksum: 032b7fb7441d8dc32be590f67a1be876 (MD5)<br>Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2017-08-15T10:55:01Z (GMT) No. of bitstreams: 1 2016_DiegoArmandoBenavidesVidal.pdf: 6256311 bytes, checksum: 032b7fb7441d8dc32be590f67a1be876 (MD5)<br>Made available in DSpace on 2017-08-15T10:55:01Z (G
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Ignat, Simon, and Filip Mattsson. "Eye Blink Detection and Brain-Computer Interface for Health Care Applications." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200571.

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Tesárek, Viktor. "Detekce mrkání a rozpoznávání podle mrkání očí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217560.

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This master thesis deals with the issues of the eye blink recognition from video. The main task is to analyse algorithms dealing with a detection of persons and make a program that could recognize the eye blink. Analysis of these algorithms and their problems are in the first part of this thesis. In the second part design and properties of my program are described. The realization of the program is based on the method of move detection using the accumulated difference frame, which helps to identify the eye areas. The eye blink detection algorithm tests a match between a tresholded pattern of t
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Books on the topic "Eye detection"

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A, Douthwaite W., and Hurst Mark A, eds. Cataract: Detection, measurement in optometric practice, and management. Butterworth Heinemann, 1993.

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1942-, Sommer Alfred, and World Health Organization, eds. Vitamin A deficiency and its consequences: A field guide to detection and control. 3rd ed. World Health Organization, 1995.

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Muir, Frank. Eye for an eye. Soho Crime, 2014.

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J, Randisi Robert, and Private Eye Writers of America., eds. The Eyes have it: The first Private Eye Writers of America anthology. Mysterious Press, 1987.

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Lee, Vincent C. E. Eye mouse system: Mouse control technique for detecting and tracking of eyes in visual images. UMIST, 1997.

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Clark, Douglas. Jewelled eye. Perennial Library, 1988.

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Brockmole, James R., and Michi Matsukura. Eye movements and change detection. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199539789.013.0031.

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Rae, Robert Andrew. Detection of eyelid position in eye-tracking systems. 2004.

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Huh, Jinny. Arresting Eye: Race and the Anxiety of Detection. University of Virginia Press, 2015.

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The arresting eye: Race and the anxiety of detection. 2015.

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Book chapters on the topic "Eye detection"

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Rathgeb, Christian, Andreas Uhl, and Peter Wild. "Eye Detection." In Advances in Information Security. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5571-4_4.

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Bessonova, Yulia V., and Alexander A. Oboznov. "Eye Movements and Lie Detection." In Intelligent Human Systems Integration. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73888-8_25.

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Park, Kang Ryoung, Jeong Jun Lee, and Jaihie Kim. "Facial and Eye Gaze Detection." In Biologically Motivated Computer Vision. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_37.

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Yan, Hongjin, and Yonglin Zhang. "Medical Eye Image Detection System." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23223-7_80.

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Shidnekoppa, Rekha A., Manjunath Kammar, and K. S. Shreedhar. "Liveness Detection Based on Eye Flicker." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9059-2_8.

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Chen, Shuo, and Chengjun Liu. "Various Discriminatory Features for Eye Detection." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28457-1_9.

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Meng, Chunning, and Taining Zhang. "Human Eye Detection via Sparse Representation." In Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49831-6_71.

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Pradhan, Ashis, Jhuma Sunuwar, Sabna Sharma, and Kunal Agarwal. "Fatigue Detection Based on Eye Tracking." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8237-5_12.

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Puri, Karuna, and Preeti Mulay. "Hawk Eye: A Plagiarism Detection System." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2517-1_20.

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Telse, Vrushabh, Rahul, and Joydeep Sengupta. "Eye Gaze Tracking and Blinking Detection." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4862-2_30.

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Conference papers on the topic "Eye detection"

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Ji, Fan, Jun Ya Wang, Jun Chang, and Yun Qiang Zhang. "Research on multilayer lobster eye detection technology." In Tenth Symposium on Novel Optoelectronic Detection Technology and Applications, edited by Chen Ping. SPIE, 2025. https://doi.org/10.1117/12.3056878.

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Patil, Saraswati, Revati Shimpi, Rushabh Rode, Sachi Chakrabarti, and Renuka Pawar. "Autism Detection Using Eye Tracking." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070486.

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Vengurlekar, Mrunal S., M. Toufeeq Nadaf, Nelton N. Fernandes, and K. M. Chaman Kumar. "Conjunctivitis Eye Detection using Deep Learning." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689984.

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Zhang, Shijie, Zhiqi Zhang, and Juan Liu. "Holographic Maxwellian near-eye display with continuous eyebox replication." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/dh.2024.tu1b.2.

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Nasra, Parul, Sheifali Gupta, and Gotte Ranjith Kumar. "Infrared Eye Image Classification: A Deep Learning Approach for Enhanced Eye State Detection." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011466.

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Rodrigues, Fredson Costa, Anselmo C. de Paiva, João Dalysson S. de Almeida, Geraldo Braz Júnior, Aristófanes Corrêa, and André Castelo Branco Soares. "Computational Methodology for Iris Segmentation and Detection in Images from the Eyes Region Using Convolutional Neural Networks." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18910.

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Eye tracking is an application of computer vision responsible for detecting the iris and pupil in the eye region. The usefulness of this tracking contributes to research that assesses cognitive aspects through pupillary reactions identified in these detected regions. Another application in this task is iris recognition in digital biometrics. This study aims to carry out the verification and detection of the iris in images of the eye region occluded by eyelashes, eyelids and specular reflexes, through a deep neural network called At-Unet in this article. In order to assist in eye tracking this
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Munteanu, Mihai, Alina Magda, Radu Ciorap, Corneliu Rusu, and Luige Vladareanu. "EOG SIGNAL PROCESSING ALGORITHM USED IN EYE MOVEMENT DETECTION." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-255.

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The EOG signal is based on the potential difference between the cornea and retina (also known as the corneo-retinal potential) and can be measured by using electrodes placed around the eyes. The amplitude of the EOG signal varies between 50 ... 3500 uV. The eye can be seen as a dipole with a positive pole in the retina and the negative one in the cornea. Thus, an electric potential field is created that is changing proportionally with the rotation of the eye. The movements can be assessed from a single eye, but because for a healthy human being the eye movements are coupled, in this paper the
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Choi, Inho, Seungchul Han, and Daijin Kim. "Eye Detection and Eye Blink Detection Using AdaBoost Learning and Grouping." In 2011 20th International Conference on Computer Communications and Networks - ICCCN 2011. IEEE, 2011. http://dx.doi.org/10.1109/icccn.2011.6005896.

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Wang, Liting, Xiaoqing Ding, Chi Fang, Changsong Liu, and Kongqiao Wang. "Eye blink detection based on eye contour extraction." In IS&T/SPIE Electronic Imaging, edited by Jaakko T. Astola, Karen O. Egiazarian, Nasser M. Nasrabadi, and Syed A. Rizvi. SPIE, 2009. http://dx.doi.org/10.1117/12.804916.

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Abdulin, Evgeniy, and Oleg Komogortsev. "User Eye Fatigue Detection via Eye Movement Behavior." In CHI '15: CHI Conference on Human Factors in Computing Systems. ACM, 2015. http://dx.doi.org/10.1145/2702613.2732812.

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Reports on the topic "Eye detection"

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Scassellati, Brian. Eye Finding via Face Detection for a Foveated, Active Vision System. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada455661.

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Okuda, Koji, Michimasa Itoh, Bunji Inagaki, Shin Yamamoto, and Satoshi Mori. Detection of Eye Blink and Gaze Direction to Estimate Driver's Condition. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0045.

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Wang, Ting Wei, Wei-Ting Luo, Yu-Kang Tu, Yu-Bai Chou, and Yu-Te Wu. Diagnostic Accuracy of Eye Art for Fundus-Based Detection of Diabetic Retinopathy: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.5.0079.

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Petrich, Jacob W. Development of Methods for the Real-Time and Rapid Identification and Detection of TSE in Living Animals Using Fluorescence Spectroscopy of the Eye. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada484430.

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Petrich, Jacob W. Development of Methods for the Real-Time and Rapid Identification and Detection of TSE in Living Animals Using Fluorescence Spectroscopy of the Eye. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada427875.

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McIntire, Lindsey, Chuck Goodyear, Justin Nelson, R. A. McKinley, and John McIntire. Eye Metrics: An Alternative Vigilance Detector. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada585834.

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McIntire, Lindsey, Chuck Goodyear, Nathaniel Bridges, et al. Eye-Tracking: An Alternative Vigilance Detector. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada559743.

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McIntire, Lindsey, R. A. McKinley, John McIntire, Chuck Goodyear, and Justin Nelson. Eye Metrics: An Alternative Vigilance Detector for Military Cyber Operators. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada590398.

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Smit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, 2022. http://dx.doi.org/10.57022/qpsm1481.

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Overview Skin cancer prevention is a component of the new Cancer Plan 2022–27, which guides the work of the Cancer Institute NSW. To lessen the impact of skin cancer on the community, the Cancer Institute NSW works closely with the NSW Skin Cancer Prevention Advisory Committee, comprising governmental and non-governmental organisation representatives, to develop and implement the NSW Skin Cancer Prevention Strategy. Primary Health Networks and primary care providers are seen as important stakeholders in this work. To guide improvements in skin cancer prevention and inform the development of th
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Judge, Seth, Seth Judge, Lauren Smith, and Richard Camp. Pacific Island landbird monitoring report, Kalaupapa National Historical Park, 2021. National Park Service, 2025. https://doi.org/10.36967/2313509.

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In 2021, landbird surveys were conducted at Kalaupapa National Historical Park on the island of Molokaʻi to assess changes in species composition, distribution, and population densities since 2005. Point-transect distance sampling surveys were conducted on six transects at 50 landbird monitoring stations within an 1,834-hectare area. A total of nine landbird species were detected, with the ʻApapane (Himatione sanguinea) the only endemic Hawaiian species detected. Sufficient detections of six species allowed for population density and abundance estimates, which were compared to 2005 estimates u
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