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Journal articles on the topic 'Eye detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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'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.  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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Katkar, Aniruddha. "EYE DISEASE RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32078.

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This paper presents an innovative system for detecting eye diseases utilizing advanced machine learning techniques. Given the increasing prevalence of eye disorders, early detection and intervention are of utmost importance. The proposed system integrates a diverse dataset comprising medical images and patient information. Deep learning algorithms are employed to extract intricate features from the dataset. These features are then input into a predictive model, facilitating accurate identification of potential eye diseases. Rigorous testing and validation demonstrate the system's performance a

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Hsu, Chih-Yu, Rong Hu, Yunjie Xiang, Xionghui Long, and Zuoyong Li. "Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation." Mathematics 10, no. 15 (2022): 2597. http://dx.doi.org/10.3390/math10152597.

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Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detecting and segmenting. In this study, we established a dataset to reduce elaboration for chipping eye images and denoting labels. An improved DeepLabv3+ network architecture (IDLN) was also proposed for applying it to the benchmark segmentation datasets. The IDLN was modified by cascading convolutional block attention modules (CBAM) with MobileNetV2.

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Huseyin, Oguzhan Tevetoglu, and Kahraman Nihan. "CROWD ANALYSIS WITH FISH EYE CAMERA." International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE) 6, no. 2/3 (2019): 1–7. https://doi.org/10.5281/zenodo.3342514.

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Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a controllable field by individuals. Therefore a technology, software products is needed. In this paper we focused on what we have done about crowd analysis and examination the problem of human detection with fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which is Haar Classification algorithm, is used to distinguish human body under different conditions. First, motion analysis is used to search for meaningful data, and then the desired object is de

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Pol, Vaishnavi, Vaishali Waghmare, Rupali Shinde, and Prof R. R. Suryavanshi. "Eye Diseases Detection using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3602–5. http://dx.doi.org/10.22214/ijraset.2024.62403.

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Abstract: This paper presents a systematic recent advancement in the application of machine learning (ML) techniques for the detection of eye diseases. With the increasing prevalence of ocular conditions worldwide, there is a growing need for automated and accurate diagnostic tools to assist clinicians in early detection and intervention. Early detection of eye diseases is critical, as individuals who have a higher risk of developing eye diseases include those with diabetes, those over 60, those with a family history of eye diseases, and those who have had eye surgery or injuries. Treatment of

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Ram, Dr C. Sunitha, D. J. V. S. Koushik, and H. Sree Pavan. "Drowsiness Detection using EAR (Eye Aspect Ratio) by Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem19675.

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Drowsiness detection is critical in many sectors, including transportation, healthcare, and workplace safety, since it may have a substantial influence on human performance and safety. Traditional sleepiness detection approaches are frequently subjective, time intensive, and unsuitable for real-time applications. In recent years, computer vision-based techniques that use eye-related characteristics to identify tiredness have shown promise. The eye aspect ratio, a geometric measure determined from ocular landmarks that indicates the openness or closure of the eyes, is one such trait. We present

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Kulkarni, Mayuresh. "AI-ACCIDENT-EYE." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 752–54. http://dx.doi.org/10.22214/ijraset.2024.64716.

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Due to the sheer rise in vehicle accidents, there is an increasing demand for effective early vehicle crash detection and alert systems. We propose a vehicle crash detection system with sophisticated components such as raspberry pi, sensors, APIs, web browser automation and communication modules to improvise vehicle crash detection performance. The existing concept presents extreme Gradient Boosting, a Machine Learning approach for identifying accidents utilizing a set of real time data. Working on this method considering single dataset such as upstream and downstream averages, results in a lo

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 35–57. http://dx.doi.org/10.4018/ijcvip.2015070103.

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Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks h

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Tran, Dang-Khoa, Thanh-Hai Nguyen, and Thanh-Nghia Nguyen. "Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm." European Journal of Engineering and Technology Research 6, no. 4 (2021): 6–12. http://dx.doi.org/10.24018/ejeng.2021.6.4.2438.

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In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast,

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Tran, Dang-Khoa, Thanh-Hai Nguyen, and Thanh-Nghia Nguyen. "Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm." European Journal of Engineering and Technology Research 6, no. 4 (2021): 6–12. http://dx.doi.org/10.24018/ejers.2021.6.4.2438.

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In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast,

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Dewi, Christine, Rung-Ching Chen, Xiaoyi Jiang, and Hui Yu. "Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks." PeerJ Computer Science 8 (April 18, 2022): e943. http://dx.doi.org/10.7717/peerj-cs.943.

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Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations

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Mrs., Smita Jawale, Pragati Malvadia Ms., and Ashwini Meena Ms. "DROWSY DRIVER DETECTION SYSTEM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 240–46. https://doi.org/10.5281/zenodo.48987.

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A Drowsy Driver Detection System is an Image processing based system. This system is developed using a non-intrusive machine vision based concepts. In this system, there is a camera that will be continuously monitoring the driver’s face to detect fatigue. In case the driver is detected as fatigue, the system issues an alarm. This system detects drowsiness by checking the amount of time the eyes are closed. The first five consecutive frames of the camera is checked, if the eyes are found closed in all the five frames, then the system issues alarm. Before determining whether eye is open or

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Birawo, Birtukan, and Pawel Kasprowski. "Review and Evaluation of Eye Movement Event Detection Algorithms." Sensors 22, no. 22 (2022): 8810. http://dx.doi.org/10.3390/s22228810.

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Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event

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Kanakambika, N. L. Preethi, Priyanka, S. Varekar Shraddha, and B. C. Anil. "An Optimised Eye Blink Detection Mechanism for Disabled Persons." Journal of Signal Processing 5, no. 3 (2019): 23–29. https://doi.org/10.5281/zenodo.3540429.

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<em>By this project, we explain an actual technique using some image and video meting out algorithms for eye blink detection. The object of this technique is that help to disabled those who cannot communicate with humans. In this we use the Haar cascade algorithm to detect the angle of eye and face for the information on the eyes and the facemask axis. To include with this, the similar classifier created on Haar's characteristics is used to discover the interaction between the eyes and the facial axis to position them. To detect the position of the observed face the effective ocular tracki

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Patil, Prof Sarika. "Drowsiness Detection using Eye Blink." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 5030–34. http://dx.doi.org/10.22214/ijraset.2018.4819.

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Et al., Ahmed. "Eye Detection using Helmholtz Principle." Baghdad Science Journal 16, no. 4(Suppl.) (2019): 1087. http://dx.doi.org/10.21123/bsj.2019.16.4(suppl.).1087.

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Eye Detection is used in many applications like pattern recognition, biometric, surveillance system and many other systems. In this paper, a new method is presented to detect and extract the overall shape of one eye from image depending on two principles Helmholtz & Gestalt. According to the principle of perception by Helmholz, any observed geometric shape is perceptually "meaningful" if its repetition number is very small in image with random distribution. To achieve this goal, Gestalt Principle states that humans see things either through grouping its similar elements or recognize patter

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Dimililer, Kamil, Yoney Kirsal Ever, and Haithm Ratemi. "Intelligent eye Tumour Detection System." Procedia Computer Science 102 (2016): 325–32. http://dx.doi.org/10.1016/j.procs.2016.09.408.

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Zhou, Zhi-Hua, and Xin Geng. "Projection functions for eye detection." Pattern Recognition 37, no. 5 (2004): 1049–56. http://dx.doi.org/10.1016/j.patcog.2003.09.006.

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Wedel, Michel, Jin Yan, Eliot L. Siegel, and Hongshuang Alice Li. "Nodule Detection with Eye Movements." Journal of Behavioral Decision Making 29, no. 2-3 (2016): 254–70. http://dx.doi.org/10.1002/bdm.1935.

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Patil, Shiva Kumar, M. Shilpa B, N. Yashaswini C, and R. Supritha K. "Eye Blink Detection – A Survey." Perspectives in Communication, Embedded-systems and Signal-processing - PiCES 5, no. 11 (2022): 110–12. https://doi.org/10.5281/zenodo.6331652.

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We live in world where nothing is impossible. The technology has come handy not only for the physically abled, but also for the disabled. Several products are being launched to help the section of the society which comprises of the physically challenged population. Blink detection based methods are being explored for the paralysed to help them. A survey in these methods is discussed in this paper.

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Vijayalaxmi, B., Kaushik Sekaran, N. Neelima, P. Chandana, Maytham N. Meqdad, and Seifedine Kadry. "Implementation of face and eye detection on DM6437 board using simulink model." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 785–91. http://dx.doi.org/10.11591/eei.v9i2.1703.

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Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generate

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B., Vijayalaxmi, Sekaran Kaushik, Neelima N., Chandana P., N. Meqdad Maytham, and Kadry Seifedine. "Implementation of face and eye detection on DM6437 board using simulink model." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 785–91. https://doi.org/10.11591/eei.v9i2.1703.

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Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locatingthe eye region failing which a warning signal is gen

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OZAWA, Takahisa, Yusuke AOTAKE, Hiroshi SHIMODA, Shogo FUKUSHIMA, and Hidekazu YOSHIKAWA. "Real-Time Eye Gaze Point and Eye Blink Detection Using Eye-Sensing HMD." Transactions of the Society of Instrument and Control Engineers 37, no. 8 (2001): 687–96. http://dx.doi.org/10.9746/sicetr1965.37.687.

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Sun, Weifeng, Yuqi Wang, Bingliang Hu, and Quan Wang. "Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal." Electronics 13, no. 10 (2024): 1798. http://dx.doi.org/10.3390/electronics13101798.

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Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected experimental data samples, and constructed experimental data sets. In this study, eye-tracking feature extraction was completed, and the significance difference of eye-tracking features under different fatigue states was discussed by two-way repeated-measures ANOVA (Analysis of Variance). The experimental results demonstrate the feasibil

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Vijaya Kumar C. N.. Suresh Kumar H. S., Rakshitha K. C., Ningappa Uppa,. "ADM- Road Eye: Advanced Traffic Sign Detection." Journal of Electrical Systems 20, no. 5s (2024): 355–65. http://dx.doi.org/10.52783/jes.1976.

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In recent years, a plethora of systems have emerged for recognizing traffic signs. This paper offers a comprehensive overview of the latest and most effective approaches in detecting and categorizing traffic signs. The primary goal of detection techniques is to pinpoint the precise areas containing traffic signs, which are classified into three main categories: color-based, shape-based, and learning-based methods of Alex net, Desnse net, and Mobil net (ADM) models. Moreover, methods of classification are divided into two groups; those relying on manually crafted features such as HOG, LBP, SIFT

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B., Vijayalaxmi, Anuradha Chavali, Sekaran Kaushik, N. Meqdad Maytham, and Kadry Seifedine. "Image processing based eye detection methods a theoretical review." Bulletin of Electrical Engineering and Informatics 9, no. 3 (2020): 1189–97. https://doi.org/10.11591/eei.v9i3.1783.

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Lately, many of the road accidents have been attributed to the driver stupor. Statistics revealed that about 32% of the drivers who met with such accidents demonstrated the symptoms of tiredness before the mishap though at varying levels. The purpose of this research paper is to revisit the various interventions that have been devised to provide for assistance to the vehicle users to avert unwarranted contingencies on the roads. The paper tries to make a sincere attempt to encapsulate the body of work that has been initiated so far in this direction. As is evident, there are numerous ways in w

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Islam, Arafat, Naimur Rahaman, and Md Atiqur Rahman Ahad. "A Study on Tiredness Assessment by Using Eye Blink Detection." Jurnal Kejuruteraan 31, no. 2 (2019): 209–14. http://dx.doi.org/10.17576/jkukm-2019-31(2)-04.

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In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 12

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Ali, Syed Sofiya, and Dr Suman Kumar Swarnkar. "Conjunctivitis Detection: A Comprehensive Review of Deep Learning Approaches." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem37957.

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Background: Conjunctivitis, often referred to as "pink eye" is a highly prevalent inflammation of the conjunctiva, a thin, transparent membrane lining the white part of your eye and the inner surface of your eyelids. This inflammation triggers a cascade of symptoms that can range from mild annoyance to significant discomfort, affecting people of all ages worldwide. The three main types of conjunctivitis are viral, bacterial, and allergy. Some common symptoms include redness of the white part of the eye, ranging from watery to thick and pus-like, discharge from the eye, itching or burning sensa

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Budiyanto, Almira, Abdul Manan, and Elvira Sukma Wahyuni. "Eye Detection System Based on Image Processing for Vehicle Safety." Techné : Jurnal Ilmiah Elektroteknika 19, no. 01 (2020): 11–22. http://dx.doi.org/10.31358/techne.v19i01.225.

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The more advanced the technology and the greater the community's need to carry out activities every day, the number of vehicles on the highway is getting crowded. From year to year, the greater the level of traffic accidents caused by many factors, among the usual reasons is the loss of awareness of the driver when driving a vehicle especially drowsiness. One of the drowsiness parameters is the frequency eye blinks. Therefore, to get the drowsiness symptoms, the purpose of this research is to detect the eye blinks, which in turn reduce the level of accidents by detecting sleepy eyes based on d

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Kalbkhani, Hashem, Mahrokh G. Shayesteh, and Seyyed Mohsen Mousavi. "Efficient algorithms for detection of face, eye and eye state." IET Computer Vision 7, no. 3 (2013): 184–200. http://dx.doi.org/10.1049/iet-cvi.2011.0091.

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Zhang, Shigui, Junhui He, and Yuanwen Zou. "YOLO Model-Based Eye Movement Detection During Closed-Eye State." Applied Sciences 15, no. 9 (2025): 4981. https://doi.org/10.3390/app15094981.

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Eye movement detection technology holds significant potential across medicine, psychology, and human–computer interaction. However, traditional methods, which primarily rely on tracking the pupil and cornea during the open-eye state, are ineffective when the eye is closed. To address this limitation, we developed a novel system capable of real-time eye movement detection even in the closed-eye state. Utilizing a micro-camera based on the OV9734 image sensor, our system captures image data to construct a dataset of eyelid images during ocular movements. We performed extensive experiments with m

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Haider, Shamil Hamid, AlKindy Bassam, H. Abbas Amel, and Basim Al-Kendi Wissam. "An intelligent strabismus detection method based on convolution neural network." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 6 (2022): 1288–96. https://doi.org/10.12928/telkomnika.v20i6.24232.

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Strabismus is one of the widespread vision disorders in which the eyes are misaligned and asymmetric. Convolutional neural networks (CNNs) are properly designed for analyzing images and detecting texture patterns. In this paper, we proposed a system that uses deep learning CNN applications for automatically detecting and classifying strabismus disorder. The proposed system includes two main stages: first, the detection of facial eye segmentation using the viola-jones algorithm. The second stage is to map the segmented eye area according to the iris position of each eye. This method is applied

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Premalatha, Mrs M., A. Heymath Kumar, M. Manoj Kumar, P. Pavithran, and K. Shatyadeep. "Drugged Eye Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1577–82. http://dx.doi.org/10.22214/ijraset.2023.50427.

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Abstract: Drugs are a major problem in economic and many losses in worldwide. In this project, an image processing approach is proposed for identifying drugged eye based on convolutional neural network. According to the CNN algorithm, eye image details are taken by the existing packages from the front end used in this project. However, it can take a few moments. So, this proposed system can be used to identify drugged eyes quickly and automatically. The eye images dataset are taken from Kaggle. These images are taken as a training set for this drugged eye detection. This proposed approach is c

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Imaduddin, Helmi, and Alivia Rahma Sakina. "Eye disease detection using transfer learning based on retinal fundus image data." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 509. http://dx.doi.org/10.11591/ijeecs.v36.i1.pp509-516.

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The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image da

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Helmi, Imaduddin Alivia Rahma Sakina. "Eye disease detection using transfer learning based on retinal fundus image data." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 509–16. https://doi.org/10.11591/ijeecs.v36.i1.pp509-516.

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The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image da

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Wang, Yuqi, Lijun Zhang, and Zhen Fang. "Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography." Algorithms 15, no. 3 (2022): 84. http://dx.doi.org/10.3390/a15030084.

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Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are e

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Prasanthi, B. Bhavya, G. Sai Mohan Reddy, N. Gnana Vardhan, I. Varshitha, N. Rushita, and Mr D. Sudheer. "Driver Drowsiness Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43459.

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Drowsy driving is a leading cause of road accidents, posing significant risks to drivers and passengers. This paper presents a real-time driver drowsiness detection system that monitors facial features to identify signs of fatigue. The system utilizes computer vision techniques, specifically Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR), to detect prolonged eye closure and yawning. A webcam captures live video, and the frames are processed using Python, OpenCV, and Dlib to track facial landmarks. When drowsiness is detected, an audio alert is triggered to warn the driver. The proposed sy

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B, R. Sandhya, S. Sahana, and S. Sneha. "Third Eye for Blind." Perspectives in Communication, Embedded-systems and Signal-processing - PiCES 4, no. 11 (2021): 280–83. https://doi.org/10.5281/zenodo.4592708.

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Independent travel is a well known challenge for blind or visually impaired persons and also the increasing availability of portable digital image devices, high performance and cost efficiency has created a vast opportunity for supplementing traditional scanning for document image acquisition. A camera based visual assistance framework for object detection, face recognition, news and book reading is built. It converts into a voice output to help the blind people. Thus, we developed this system from a single camera-captured video streaming for the visually impaired persons, so they can lead the

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S, Suriya, Anubharathi V S, Bharathi G, Harshada R, Vignesh Aditya R, and Sundarsree B G. "Oculang: Empowering Communication through Blink Language Detection." IRO Journal on Sustainable Wireless Systems 6, no. 4 (2025): 318–32. https://doi.org/10.36548/jsws.2024.4.003.

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Oculang is a communication system that uses computer vision and machine learning techniques to enable individuals with neurodegenerative disorders to communicate using eye gestures. The application focuses on detecting facial landmarks, gaze estimation, and blink detection to analyze combinations of eye movements captured from video input and produce message outputs. Dlib’s shape predictor (68 facial landmarks) and OpenCV-based image processing methods are used to extract and process the features of the eye region. A decision-making algorithm maps the detected gestures and predefined keywords

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Aynur Jabiyeva, Rashad Khalilov, Aynur Jabiyeva, Rashad Khalilov. "EXAMINATION OF THE CONTROL SYSTEM OF AN ARTIFICIAL EYE IMPLANT." PIRETC-Proceeding of The International Research Education & Training Centre 24, no. 03 (2023): 127–34. http://dx.doi.org/10.36962/piretc24032023-127.

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Due to various reasons, a person who is missing one eye may experience psychological as well as excruciating suffering. Enucleation and evisceration surgery are the most often used methods to remove a sick or injured eye. The patient is often fitted with a bespoke implant into the orbital tissues after the surgeon removes the eye. In order to keep the socket from looking hollow and depressed, this replaces volume. Once the socket has stabilized, a prosthetic shell—also known as an artificial eye, glass eye, or ocular prosthesis—is placed within. An ocular implant can mechanically replace the l

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Nie, Yang, Taibo Liu, and Long Han. "Eye Fatigue Detection System Design and Implementation." Computer Life 12, no. 2 (2024): 11–13. http://dx.doi.org/10.54097/at3ba893.

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This paper implements the detection and recognition of eye fatigue state based on artificial intelligence technology and computer vision. Through the detection and recognition of eye closure time, the system provides voice warning information to prompt the driver, so as to reduce the traffic accidents caused by fatigue driving. The system is connected to a night vision camera to achieve real-time face detection and recognition. On this basis, the system locates the driver's eye feature points and calculates the parameters corresponding to the eye feature state, which is used as the basis for e

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