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Journal articles on the topic 'Eye detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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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 conditions, wearing glasses, having facial hair or moustache on the face, and varying orientation poses or occlusion of the face. We categorize face detection models and look at basic face detection methods in this paper. We categorize face detection models and look at basic face detection methos in this paper. Then we’ll go through eye detection and estimation techniques. Keywords: Image Processing, Face Detection, Eye Detection, Gaze Estimation

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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 estimate the eye blinking frequency and the eye closure duration. On the other hand, mouth analysis and tracking the yawning frequency of a driver is an alternative way of detecting the drowsy driver. These techniques will identify the drowsing state of the driver and if he is drowsy, then an alert message is sent to the driver stating that the driver is no longer capable of driving the vehicle safely thus preventing accidents.

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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 subjected to preprocessing steps to ensure optimal quality. The trained models learn from these datasets, enabling them to accurately classify eye conditions. To facilitate ease of use, we have developed an intuitive interface that allows ophthalmologists to input eye images from scans. Leveraging the power of Deep Learning algorithms, the system swiftly analyzes the images and generates comprehensive reports indicating the presence of various eye diseases and cancer. This approach addresses the limitations of traditional diagnostic methods, as it significantly reduces the time and effort required for disease identification. By incorporating advanced Deep Learning techniques, our system achieves the highest levels of accuracy in detecting and diagnosing eye diseases, ensuring prompt and effective treatment for patients. Overall, our project showcases the potential of Deep Learning in revolutionizing the detection and diagnosis of eye diseases and cancer, ensuring that patients receive prompt and accurate treatment regardless of their geographical location.

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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's face and eyes are the primary targets of the image processing used in this model. The device takes a picture of the driver's face and uses eye tracking data to guess when he or she will blink. To quantify perclos, we use an algorithm to follow and analyse the driver's face and eyes. A warning tone is played if the blink rate is too high.</em>

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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 but this approach is completely non-intrusive which does not affect the driver in any way, hence giving the exact condition of the driver. For detection of drowsiness the per closure value of eye is considered. So when the closure of eye exceeds a certain amount then the driver is identified to be sleepy. For implementing this system several OpenCv libraries are used including Haar-cascade. The entire system is implemented using microcontroller.    

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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 this system is that the algorithm works without any constraint of the background as the face is detected using a skin segmentation technique. The detection performance of this system was tested using video images which were recorded under laboratory conditions. The applicability of the system is discussed in light of fatigue detection for drivers.

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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 and its ability to provide reliable predictions. The early diagnosis enabled by this system has the potential to significantly impact patient outcomes and contribute to the advancement of ophthalmic healthcare. The Eye Disease Detection System serves as a valuable tool for the early detection and management of various eye conditions. Through the integration of advanced technologies such as machine learning and medical imaging, this system enhances the accuracy and efficiency of the diagnostic process. Index Terms : Vision disorders, Glaucoma, Macular degeneration, Eye diseases, Ophthalmology, Corneal diseases.

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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 detected by the trained classifier. Significant data has been sent to the end user via socket programming and human density analysis is presented.

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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. Experiments were carried out to verify the effectiveness of the EIMDSD dataset in human eye image detection and segmentation with different deep learning models. The result shows that the IDLN model achieves the appropriate segmentation accuracy for both eye images, while the UNet and ISANet models show the best results for the left eye data and the right eye data among the tested models.

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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 eye diseases and the avoidance of irreversible vision loss depends heavily on early detection and prompt intervention. The prevention or deceleration of vision loss and blindness is largely dependent on the early detection of eye diseases. Many eye diseases, such as glaucoma, cataracts, and diabetic retinopathy, unfortunately, have no early warning signs or symptoms.

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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 a sleepiness detection method in this research that blends eye aspect ratio computation with machine learning techniques to provide real-time and accurate drowsiness evaluation. We offer a thorough technique that includes calculating the eye-aspect ratio, extracting features, and classifying them with machine learning methods. The ability of our proposed technique is evaluated using a dataset of eye pictures acquired from individuals under various sleepiness situations. Our experimental findings show that our technique is successful in detecting sleepiness with good accuracy, sensitivity, and specificity. Our suggested method includes applications such as sleepy driving detection, tiredness monitoring in hospital settings, and workplace safety. This research advances the area of sleepiness detection by combining the ocular aspect ratio and machine learning to measure tiredness in real time. KEYWORDS: Drowsiness detection, Eye aspect ratio, Machine learning, Eye tracking, Blink rate, Pupil dilation, Real-time monitoring, Non-invasive, Safety, Transportation

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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 lower efficiency. While most existing vehicle crash detection systems depend on single modal data, our proposed vehicle crash detection system uses an ensemble machine learning model based on multi modal data such as accelerometer, gyroscope, and shock sensor data. In order to verify that an accident has happened, a message is sent to the user, if the user does not respond to the call within 30 seconds, our detection system immediately conveys the exact location of the accident occurred to the nearest hospital within 82 seconds via voice message. Also another voice message enclosing the information about the location of the accident along with the hospital name will be sent to the trusted person specified by the user. The experimental results indicate that the proposed vehicle crash detection system performs significantly better than single classifiers. With the help of this application, we preserve the state of humanity and generate harmony in our society.

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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 have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.

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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, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

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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 and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.

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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 closed, it is required to determine eye location. To determine eye location, the images captured from the camera are binarized. This binary version of the image help to find the edges of the face, which narrows the area of where the eyes may exist. Once the face area is found, the eyes are found by computing the horizontal averages in the area. Based on intensity change eye location is found, as eye regions in the face present great intensity changes. Once the eyes are located, measuring the distances between the intensity changes in the eye area it is determined whether the eyes are open or closed. A large distance corresponds to eye closure. 

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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 detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods.

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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 tracking technique is used. Lastly, to check mobile phones with Android, a closed eye detection is used based on the condition of the eyelids (closed or open). This technique is used with and without a level filter to show the development of finding precision. This request is used in day to day life to study the result of light and the distance among the eyes and the mobile to evaluate the detection accuracy and overall accuracy of the proposed system. This test results show that our proposed technique gives an all-out precision of 98% and a 100% location exactness over a separation of 35 cm and a counterfeit light.</em>

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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 patterns. In general, according to Gestalt Principle, humans see things through general description of these things. This paper utilizes these two principles to recognize and extract eye part from image. Java programming language and OpenCV library for image processing are used for this purpose. Good results are obtained from this proposed method, where 88.89% was obtained as a detection rate taking into account that the average execution time is about 0.23 in seconds.

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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 generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

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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 generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

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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 feasibility of eye fatigue detection from eye-tracking signals. In addition, this study considers the effects of different feature extraction methods on eye fatigue detection accuracy. This study examines the performance of machine learning algorithms based on manual feature calculation (SVM, DT, RM, ET) and deep learning algorithms based on automatic feature extraction (CNN, auto-encoder, transformer) in eye fatigue detection. Based on the combination of the methods, this study proposes the feature union auto-encoder algorithm, and the accuracy of the algorithm for eye fatigue detection on the experimental dataset is improved from 82.4% to 87.9%.

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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, SURF, BRISK, and those leveraging deep learning. The paper summarizes various detection and classification methods, along with the datasets utilized, for quick reference. Additionally, it provides suggestions for future research directions and recommendations to enhance traffic sign recognition performance..

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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 which one can identify the fatigue of the driver, namely biotic or physiological gauges, vehicle type and more importantly the analysis of the face in terms of its alignment and other attributes.

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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 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy.

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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 sensation, and a Gritty feeling in the eye. Methods: Several deep-learning techniques for conjunctivitis detection have been developed due to their simplicity of use and their affordability. This systematic review delves into the burgeoning field of machine learning within healthcare, specifically seeking viable approaches for detecting conjunctivitis. We embark on a comparative analysis of the most successful ML algorithms currently in use regarding machine learning, including evaluation metrics, image augmentation, and the origin and size of the dataset used. Results: The results of this study provide compelling evidence for the feasibility and potential benefits of using DL algorithms for conjunctivitis detection. Conclusion: This review sheds light on the potential of machine learning in detecting Conjunctivitis, providing scientific evidence for its feasibility. By analyzing images, diagnoses, and clinical data within the medical field, the review explores how machine and deep learning algorithms can offer a wide-ranging approach to conjunctivitis detection.

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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 multiple versions of the YOLO algorithm, including v5s, v8s, v9s, and v10s, in addition to testing different sizes of the YOLO v11 model (n < s < m < l < x), to achieve optimal performance. Ultimately, we selected YOLO11m as the optimal model based on its highest AP0.5 score of 0.838. Our tracker achieved a mean distance error of 0.77 mm, with 90% of predicted eye position distances having an error of less than 1.67 mm, enabling real-time tracking at 30 frames per second. This study introduces an innovative method for the real-time detection of eye movements during eye closure, enhancing and diversifying the applications of eye-tracking technology.

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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 digital image processing. The method used to detect both eyes is the Viola-Jones method. The detection of both eyes can also acquire the duration of closed eyes and the number of eye blinks. A person can be said to be sleepy by means of sleepiness parameters determined by a study. The research shows that detection of eye blinks using the Viola-Jones method has a fairly high accuracy of up to 84.72% if the face condition is upright and tilted no more than 45 degrees. Another conclusion is that eye detection and driver detection are more effective at certain light intensity values which are around 2-33 lux.

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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 to three strabismus datasets, gathered as digital images. The second section covers the segmentation of the eye region. Besides, the evaluation equations for measuring system performance. The system has undergone numerous experiments in various stages to simulate and analyze the detection performance of CNN layers through different classifiers and variant thresholds ratio. The researchers investigated the experimental outcomes during the training and testing phases and obtained promising results that exhibit the effectiveness of the proposed system. According to the results, the accuracy of this technique reached 95.62%.

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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 composed of the following main steps that getting input image, Image Preprocessing, identifying reddish places, highlight those affected places, Verifying training set, showing result. Few types of eyes like drugged socially may missed to identify. This approach was tested according to drugged eye type and its' stages, such as drug consumed and not consumed. The algorithm was used for detecting the white area of eye present in given input image. Images were provided for training, such as drugged eye images and normal eye images. Before the image processing, images were converted to color models, because of find out the most suitable color model for this approach. Local Binary Pattern was used for feature extraction and Support erosion method was used for creating the model. According to this approach, drugged eyes can be identified in the average accuracy of 95%.

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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 data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases.

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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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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 system is non-intrusive, cost-effective, and can be integrated into vehicles for enhanced safety. Future improvements may incorporate machine learning models for greater accuracy and adaptability to different environments. Key Words: Drowsiness detection, computer vision, Eye Aspect Ratio, OpenCV, real-time monitoring, driver safety.

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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 extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.

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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 their lives with some independence.

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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 for message generation. The application supports both real-time video capturing and uploading video through a Django-based user interface. Experimental evaluation on recorded datasets of eye movements demonstrated robust performance in accurately recognizing blinks, winks, and gaze directions, validating the system’s reliability.

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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 lost eye. There have been significant developments in this field. To replace the missing eye, an ocular prosthesis was developed. Physically, the prosthetic seems natural. The eye, however, is stationary or just slightly mobile. development of an independent ocular motor system is the objective of this study in order to give the artificial eye more realistic movement. The detection of natural eye movement is a crucial issue. This study includes an overview of eye movement detecting techniques. Then eye movement detection using the fusion approach is created. The first aspect that is recorded and stored is the eye movement. Then, during the experiment, the sensor array yields the eye movement signal, and the matching rule yields the eye position. The experimental system, fusion technology, and early findings are covered in the majority of this work. Keywords: Sensor array, fusion, artificial eye, orbital implant, and ocular control.

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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 eye fatigue detection. When the driver has closed eyes (non-blinking) driving, the system will issue voice warning and upload the image of fatigue state in real time using the cloud platform.

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