Journal articles: 'Eye detection'

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Patil, Vaibhavi, Sakshi Patil, Krishna Ganjegi, and Pallavi Chandratre. "Face and Eye Detection for Interpreting Malpractices in Examination Hall." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 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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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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Nadella, Bhargavi. "Eye Detection and Tracking and Eye Gaze Estimation." Asia-pacific Journal of Convergent Research Interchange 1, no. 2 (June 30, 2015): 25–42. http://dx.doi.org/10.21742/apjcri.2015.06.04.

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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 (November 1, 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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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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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 (July 26, 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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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 (July 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 (May 11, 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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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 (May 11, 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, 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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Patil, Prof Sarika. "Drowsiness Detection using Eye Blink." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 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.) (December 18, 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 (May 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 (February 17, 2016): 254–70. http://dx.doi.org/10.1002/bdm.1935.

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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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Birawo, Birtukan, and Pawel Kasprowski. "Review and Evaluation of Eye Movement Event Detection Algorithms." Sensors 22, no. 22 (November 15, 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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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 (April 1, 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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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 (June 2013): 184–200. http://dx.doi.org/10.1049/iet-cvi.2011.0091.

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Singh, Samarth. "Real Time Face Liveliness Detection Using Eye Blinking and Illumination Techniques." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 847–59. http://dx.doi.org/10.37200/ijpr/v24i5/pr201756.

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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 (October 31, 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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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 (April 30, 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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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 (April 14, 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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Ghodpage, Motiksha. "Detection of Eye Cataract using MATLAB." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 1417–20. http://dx.doi.org/10.22214/ijraset.2021.33971.

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YU, Mingxin, Yingzi LIN, and Xiangzhou WANG. "An efficient hybrid eye detection method." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 1586–603. http://dx.doi.org/10.3906/elk-1312-150.

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Quang Thao, Le, Duong Duc Cuong, Vu Manh Hung, Le Thanh Vinh, Doan Trong Nghia, Dinh Ha Hai, and Nguyen Nhan Nhi. "Eye Strain Detection During Online Learning." Intelligent Automation & Soft Computing 35, no. 3 (2023): 3517–30. http://dx.doi.org/10.32604/iasc.2023.031026.

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Nanaa. "EYE DETECTION USING COMPOSITE CROSS-CORRELATION." American Journal of Applied Sciences 10, no. 11 (November 1, 2013): 1448–56. http://dx.doi.org/10.3844/ajassp.2013.1448.1456.

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Jumb, Prof Vijay, Charles Nalka, Hasan Hussain, and Ricky Mathews. "Morse Code Detection Using Eye Blinks." International Journal Of Trendy Research In Engineering And Technology 05, no. 01 (2021): 33–37. http://dx.doi.org/10.54473/ijtret.2021.5105.

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Ruiz-Beltrán, Camilo A., Adrián Romero-Garcés, Martín González, Antonio Sánchez Pedraza, Juan A. Rodríguez-Fernández, and Antonio Bandera. "Real-time embedded eye detection system." Expert Systems with Applications 194 (May 2022): 116505. http://dx.doi.org/10.1016/j.eswa.2022.116505.

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SanilM, Rithvika, S. Saathvik, Rithesh RaiK, and Srinivas P M. "DEEPFAKE DETECTION USING EYE-BLINKING PATTERN." International Journal of Engineering Applied Sciences and Technology 7, no. 3 (July 1, 2022): 229–34. http://dx.doi.org/10.33564/ijeast.2022.v07i03.036.

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Deep learning algorithms have become so potent due to increased computing power that it is now relatively easy to produce human-like synthetic videos, sometimes known as & quot; deep fakes. & quot; It is simple to imagine scenarios in which these realistic face switched deep fakes are used to extort individuals, foment political unrest, and stage fake terrorist attacks. This paper provides a deep learning strategy novel for the efficient separation of fraudulent films produced by AI from actual ones. Automatically spotting replacement and recreation deep fakes is possible with our technology. To combat artificial intelligence, we are attempting to deploy artificial intelligence. The framelevel characteristics are extracted by our system using a Res-Next Convolution neural network, and later these features are applied to train an LSTM-based recurrent neural network to determine if those submitted video is being altered in any way or not, i.e. whether it is a deep fake or authentic video. We test our technique on a sizable quantity of balanced and mixed data sets created by combining the different accessible data sets, such as Face-Forensic++[1], Deep fake detection challenge[2], and Celeb-DF[3], in order to simulate real-time events and improve the model's performance on real-time data.

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Lee, Samantha Sze-Yee, Alex A. Black, Philippe Lacherez, and Joanne M. Wood. "Eye Movements and Road Hazard Detection." Optometry and Vision Science 93, no. 9 (September 2016): 1137–46. http://dx.doi.org/10.1097/opx.0000000000000903.

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Mingli Song, Dacheng Tao, Zhuo Sun, and Xuelong Li. "Visual-Context Boosting for Eye Detection." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, no. 6 (December 2010): 1460–67. http://dx.doi.org/10.1109/tsmcb.2010.2040078.

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Takahashi, Susumu, Hiromi Kameda, and Takaaki Yamamoto. "Data Detection from Deteriorated Eye Patterns." IEEE Transactions on Consumer Electronics CE-31, no. 3 (August 1985): 378–85. http://dx.doi.org/10.1109/tce.1985.289950.

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Choi, Inho, and Daijin Kim. "Generalized Binary Pattern for Eye Detection." IEEE Signal Processing Letters 20, no. 4 (April 2013): 343–46. http://dx.doi.org/10.1109/lsp.2013.2247396.

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Ji, Qiang, Harry Wechsler, Andrew Duchowski, and Myron Flickner. "Special issue: eye detection and tracking." Computer Vision and Image Understanding 98, no. 1 (April 2005): 1–3. http://dx.doi.org/10.1016/j.cviu.2004.07.006.

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Sree, Dr L. Padma, and G. Vijaya Bharghavi. "VLSI Implementation of Eye Detection System." International Journal of Advanced Engineering Research and Science 4, no. 1 (2017): 265–69. http://dx.doi.org/10.22161/ijaers.4.1.43.

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Nakano, T. "System for driver's eye movement detection." JSAE Review 16, no. 1 (January 1995): 74–76. http://dx.doi.org/10.1016/0389-4304(94)00054-w.

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Syahputra, Eswin, Irpan Nursukmi, Sony Putra, Bayu Sukma Sani, and Rian Farta Wijaya. "EYE ASPECT RATIO ADJUSTMENT DETECTION FOR STRONG BLINKING SLEEPINESS BASED ON FACIAL LANDMARKS WITH EYE-BLINK DATASET." ZERO: Jurnal Sains, Matematika dan Terapan 6, no. 2 (February 10, 2023): 147. http://dx.doi.org/10.30829/zero.v6i2.14751.

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<span lang="id">Blink detection is an important technique in a variety of settings, including facial motion analysis and signal processing. However, automatic blink detection is challenging due to its blink rate. This paper proposes a real-time method for detecting eye blinks in a video series. The method is based on automatic facial landmark detection trained on real-world datasets and demonstrates robustness against various environmental factors, including lighting conditions, facial emotions, and head position. The proposed algorithm calculates the position of facial landmarks, extracts scalar values using the Eye Aspect Ratio (EAR), and characterises eye proximity in each frame. For each video frame, the proposed method calculates the location of the facial landmark and extracts the vertical distance between the eyelids using the position of the facial landmark. Blinks are detected by using the EAR threshold value and recognising the pattern of EAR values in a short temporal window. According to the results from a common data set, it is shown that the proposed approach is more efficient than state-of-the-art techniques.</span>

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Spourgeon, V. Andrew. "Driver Drowsiness Detection using Haar Cascade." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3598–604. http://dx.doi.org/10.22214/ijraset.2022.43815.

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Abstract: The drowsiness of a person driving a vehicle is the primary cause of accidents all over the world. Due to lack of sleep and tiredness, fatigue and drowsiness are common among many drivers, which often leads to road accidents. Alerting the driver ahead of time is the best way to avoid road accidents caused by drowsiness. In this work, two ways are used to detect the drowsiness of a person effectively. First Driver face is captured and eye retina detection and facial feature extraction are done. We propose new detection methods using deep learning techniques. To estimate the driver’s state, we use Facial and Eye regions for detecting drowsiness.

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Azimi Sotudeh, Mohammad Ali, Hasan Ziafat, and Said Ghafari. "Pupil Detection in Facial Images with Using Bag of Pixels." Advanced Materials Research 468-471 (February 2012): 2941–48. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2941.

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To detect and track eye images, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using bag of pixels, this will cause the images background to be non effective in our next steps. We used from horizontal projection, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. With a pixel of the iris or pupil can be achieved center of pupil. To find the center of pupil can be used line intersection method in the next step, we perform eye tracking. The proposed method achieve a correct eye detection rate of 97.3% on testing set that gathered from different images of face data. Moreover, in the case of glasses the performance is still acceptable.

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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 (March 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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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 (May 15, 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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Koma, Hiroaki, Taku Harada, Akira Yoshizawa, and Hirotoshi Iwasaki. "Detecting Cognitive Distraction using Random Forest by Considering Eye Movement Type." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 1 (January 2017): 16–28. http://dx.doi.org/10.4018/ijcini.2017010102.

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Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

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Baskaran, S., L. Mubark Ali, A. Anitharani, E. Annal Sheeba Rani, and N. Nandhagopal. "Pupil Detection System Using Intensity Labeling Algorithm in Field Programmable Gate Array." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5364–67. http://dx.doi.org/10.1166/jctn.2020.9429.

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Pupil detection techniques are an essential diagnostic technique in medical applications. Pupil detection becomes more complex because of the dynamic movement of the pupil region and it’s size. Eye-tracking is either the method of assessing the point of focus (where one sees) or the orientation of an eye relative to the head. An instrument used to control eye positions and eye activity is the eye tracker. As an input tool for human-computer interaction, eye trackers are used in research on the visual system, in psychology, psycholinguistics, marketing, and product design. Eye detection is one in all the applications in the image process. This is very important in human identification and it will improve today’s identification technique that solely involves the eye detection to spot individuals. This technology is still new, only a few domains are applying this technology as their medical system. The proposed work is developing an eye pupil detection method in real-time, stable, using an intensity labeling algorithm. The proposed hardware architecture is designed using the median filter, segmentation using the threshold process, and morphology to detect pupil shape. Finally, an intensity Labeling algorithm is done to locate an exact eye pupil region. A Real-time FPGA implementation is done by Altera Quartus II software with cyclone IV FPGA.

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Asghar, Rabia, Ahmad Hassan, Naveed Ur Rehman Junejo, Farwa Ikram, and Abeera Mahfooz Cheema. "Drowsiness Detection and Alertness Using Eye Motion Monitoring." Sir Syed University Research Journal of Engineering & Technology 13, no. 1 (June 28, 2023): 101–6. http://dx.doi.org/10.33317/ssurj.573.

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The driver felt sleepy when they don’t take proper rest while driving on long routes. The restless driving careless mistake could be fatal to driver as well as others’ lives. This issue has been increased to such a level that a system required to avoid accidents and save life. The driver alertness detection can play a significant role to avoid such hazards. The system can identify the drowsiness on the face of driver and can generate an alarm for them to stop or take necessary actions. Eye state analysis is a key step for alertness detection that helps to identify the state of the eye whether it is open or close. In this paper, the method has been proposed for eye state analysis following face and eye detection to detect driver’s alertness. This system has been integrated into a four-steps includes detection of face, detection of eye, analysis of eye state, and decision regarding driver's drowsiness. A warning signal has been buzzed on drowsiness detection to alarm the driver. Simulation results validate that our proposed idea attains high accuracy and low error rate as compared to state-of-art.

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Sigari, Mohamad-Hoseyn, Mahmood Fathy, and Mohsen Soryani. "A Driver Face Monitoring System for Fatigue and Distraction Detection." International Journal of Vehicular Technology 2013 (January 14, 2013): 1–11. http://dx.doi.org/10.1155/2013/263983.

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Driver face monitoring system is a real-time system that can detect driver fatigue and distraction using machine vision approaches. In this paper, a new approach is introduced for driver hypovigilance (fatigue and distraction) detection based on the symptoms related to face and eye regions. In this method, face template matching and horizontal projection of top-half segment of face image are used to extract hypovigilance symptoms from face and eye, respectively. Head rotation is a symptom to detect distraction that is extracted from face region. The extracted symptoms from eye region are (1) percentage of eye closure, (2) eyelid distance changes with respect to the normal eyelid distance, and (3) eye closure rate. The first and second symptoms related to eye region are used for fatigue detection; the last one is used for distraction detection. In the proposed system, a fuzzy expert system combines the symptoms to estimate level of driver hypo-vigilance. There are three main contributions in the introduced method: (1) simple and efficient head rotation detection based on face template matching, (2) adaptive symptom extraction from eye region without explicit eye detection, and (3) normalizing and personalizing the extracted symptoms using a short training phase. These three contributions lead to develop an adaptive driver eye/face monitoring. Experiments show that the proposed system is relatively efficient for estimating the driver fatigue and distraction.

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Han, Young-Joo, Wooseong Kim, and Joon-Sang Park. "Efficient Eye-Blinking Detection on Smartphones: A Hybrid Approach Based on Deep Learning." Mobile Information Systems 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/6929762.

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We propose an efficient method that can be used for eye-blinking detection or eye tracking on smartphone platforms in this paper. Eye-blinking detection or eye-tracking algorithms have various applications in mobile environments, for example, a countermeasure against spoofing in face recognition systems. In resource limited smartphone environments, one of the key issues of the eye-blinking detection problem is its computational efficiency. To tackle the problem, we take a hybrid approach combining two machine learning techniques: SVM (support vector machine) and CNN (convolutional neural network) such that the eye-blinking detection can be performed efficiently and reliably on resource-limited smartphones. Experimental results on commodity smartphones show that our approach achieves a precision of 94.4% and a processing rate of 22 frames per second.

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Kang, Dongwoo, and Jingu Heo. "Content-Aware Eye Tracking for Autostereoscopic 3D Display." Sensors 20, no. 17 (August 25, 2020): 4787. http://dx.doi.org/10.3390/s20174787.

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This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.

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Erdin, Muh, and Prof Lalitkumar Patel. "Early Detection of Eye Disease Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2683–90. http://dx.doi.org/10.22214/ijraset.2023.50737.

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Abstract: The eyes perform an essential part of life. The eye is one of the organs that assist humans learn about their natural environments and collect information from them. Almost everywhere in the world, the frequency of eye disease has increased, needing a serious response. Immediate eye detection will be invaluable assistance in offering further treatment to prevent blindness. In this study, a Convolution Neural Network model is used for identifying eye diseases. This research aims to categorize human eyes into four categories: trachoma, conjunctivitis, cataract, and healthy. The investigation got an accuracy of 88.36%, while the CNN model evaluation provided Precision of 89.25%, Recall of 88.75%, and F1 Score of 88.5%. Based on the accuracy and evaluation results, this system can be used for the early detection of multiple eye diseases. Several random samples were also used for testing in this investigation. The test results indicate that this system is functional.

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Divya, S., and A. D. Dhivya. "Human Eye Pupil Detection Technique Using Circular Hough Transform." International Journal of Advance Research and Innovation 7, no. 2 (2019): 72–76. http://dx.doi.org/10.51976/ijari.721911.

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Eye tracking refers to measure gaze positions and movement to reveal what individuals are looking at. Thanks to the advances of eye tracking technology, there are growing numbers of research focus in using eye tracking to study human behavior. In order to improve the accuracy of the eye gaze tracking technology, this paper presents a novel pupil detection algorithm based on intensity level with canny edge detection technique. Field programmable logic array (FPGA) based hardware implementation of the proposed technique is presented, which can be used in iris localization system on FPGA based platforms for iris recognition application.Threshold based pupil detection algorithm was found to be most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human’s eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region.

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Safarov, Furkat, Farkhod Akhmedov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov, and Young Im Cho. "Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety." Sensors 23, no. 14 (July 17, 2023): 6459. http://dx.doi.org/10.3390/s23146459.

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Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the most effective approaches for the detection of drowsiness. Robust and accurate drowsiness detection systems can be developed by leveraging deep learning to learn complex coordinate patterns using visual data. Deep learning algorithms have emerged as powerful techniques for drowsiness detection because of their ability to learn automatically from given inputs and feature extractions from raw data. Eye-blinking-based drowsiness detection was applied in this study, which utilized the analysis of eye-blink patterns. In this study, we used custom data for model training and experimental results were obtained for different candidates. The blinking of the eye and mouth region coordinates were obtained by applying landmarks. The rate of eye-blinking and changes in the shape of the mouth were analyzed using computer-vision techniques by measuring eye landmarks with real-time fluctuation representations. An experimental analysis was performed in real time and the results proved the existence of a correlation between yawning and closed eyes, classified as drowsy. The overall performance of the drowsiness detection model was 95.8% accuracy for drowsy-eye detection, 97% for open-eye detection, 0.84% for yawning detection, 0.98% for right-sided falling, and 100% for left-sided falling. Furthermore, the proposed method allowed a real-time eye rate analysis, where the threshold served as a separator of the eye into two classes, the “Open” and “Closed” states.

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Journal articles on the topic 'Eye detection'

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