Relevant bibliographies by topics / Eye state detection

Contents

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

Academic literature on the topic 'Eye state detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Eye state 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 (June 2013): 184–200. http://dx.doi.org/10.1049/iet-cvi.2011.0091.

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Lin, Lizong, Chao Huang, Xiaopeng Ni, Jiawen Wang, Hao Zhang, Xiao Li, and Zhiqin Qian. "Driver fatigue detection based on eye state." Technology and Health Care 23, s2 (June 17, 2015): S453—S463. http://dx.doi.org/10.3233/thc-150982.

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Li, Rui, Xin Wang, Jian Chun Jiang, and Hong Yun Yang. "Eye State Detection Based on Embedded Linux System." Applied Mechanics and Materials 457-458 (October 2013): 1253–56. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1253.

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Eye state detection is dramatically influenced by the position of iris, for this reason, this paper proposed an eye state detection method combined the area between the eyelid with the eyelid contour. By modifying and transplanting V4L-utils and OpenCV image processing library, video capture and display software is built on the Cortex-A8 embedded system. Through experimental verification, the embedded system can realize the acquisition, processing and display of the video stream and the eye state detection algorithm also has high accuracy.
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Wei Sun, Xiaorui Zhang, Wei Zhuang, and Huiqiang Tang. "Driver Fatigue Driving Detection Based on Eye State." International Journal of Digital Content Technology and its Applications 5, no. 10 (October 31, 2011): 307–14. http://dx.doi.org/10.4156/jdcta.vol5.issue10.36.

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Sun, Chao, Jian Hua Li, Yang Song, and Lai Jin. "Real-Time Driver Fatigue Detection Based on Eye State Recognition." Applied Mechanics and Materials 457-458 (October 2013): 944–52. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.944.

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One of the important causes of traffic accidents is driver fatigue. In this paper, a new real-time non-intrusive method to detect driver fatigue is proposed. Firstly, face region is detected by AdaBoost algorithm because of its robustness. Then a region of interest of the eye is defined based on face geometry. In this region, eye pupil is precisely located by radial symmetry transform. With principal component analysis (PCA), three eigen spaces are trained to recognize eye states. Open, closed eye samples and other non-eye samples in the face region are used to get these eigen spaces. At last, PERCLOS and consecutive eye closure time are adopted to detect driver fatigue. Experiments with thirty two participants in realistic driving condition show the reliability and the robustness of our system.
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Gou, Chao, Yue Wu, Kang Wang, Kunfeng Wang, Fei-Yue Wang, and Qiang Ji. "A joint cascaded framework for simultaneous eye detection and eye state estimation." Pattern Recognition 67 (July 2017): 23–31. http://dx.doi.org/10.1016/j.patcog.2017.01.023.

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Zhu, Xu Guang, Yin Pan Long, Lei Bang Jun, Zou Yao Bin, and Yang Ji Quan. "Eye Region Activity State based Face Liveness Detection System." International Journal of Security and Its Applications 10, no. 1 (January 31, 2016): 361–74. http://dx.doi.org/10.14257/ijsia.2016.10.1.33.

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Bai, Ou, Masatoshi Nakamura, Akio Ikeda, and Hiroshi Shibasaki. "Automatic detection of eye state for background EEG interpretation." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4307–12. http://dx.doi.org/10.1016/s1474-6670(17)56734-8.

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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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Wu, Wei. "Driver Fatigue Detection Based on Eye Locating Algorithm." Advanced Materials Research 998-999 (July 2014): 855–59. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.855.

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A method for driver fatigue detection based on eye locating was researched in this paper.. The eye location was achieved by combining gray information with shape information, and matched the eye template of image with which was in the open state. To observe images within a certain time interval was to identify the open or closed state of the drivers' eyes, so as to determine if they have fatigue driving. The results showed that the algorithm could suppress gaussian noise and impulse noise very effectively, and had better filtering performance than the standard median filters..
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Dissertations / Theses on the topic "Eye state detection"

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Husseini, Orabi Ahmed. "Multi-Modal Technology for User Interface Analysis including Mental State Detection and Eye Tracking Analysis." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36451.

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We present a set of easy-to-use methods and tools to analyze human attention, behaviour, and physiological responses. A potential application of our work is evaluating user interfaces being used in a natural manner. Our approach is designed to be scalable and to work remotely on regular personal computers using expensive and noninvasive equipment. The data sources our tool processes are nonintrusive, and captured from video; i.e. eye tracking, and facial expressions. For video data retrieval, we use a basic webcam. We investigate combinations of observation modalities to detect and extract affective and mental states. Our tool provides a pipeline-based approach that 1) collects observational, data 2) incorporates and synchronizes the signal modality mentioned above, 3) detects users' affective and mental state, 4) records user interaction with applications and pinpoints the parts of the screen users are looking at, 5) analyzes and visualizes results. We describe the design, implementation, and validation of a novel multimodal signal fusion engine, Deep Temporal Credence Network (DTCN). The engine uses Deep Neural Networks to provide 1) a generative and probabilistic inference model, and 2) to handle multimodal data such that its performance does not degrade due to the absence of some modalities. We report on the recognition accuracy of basic emotions for each modality. Then, we evaluate our engine in terms of effectiveness of recognizing basic six emotions and six mental states, which are agreeing, concentrating, disagreeing, interested, thinking, and unsure. Our principal contributions include the implementation of a 1) multimodal signal fusion engine, 2) real time recognition of affective and primary mental states from nonintrusive and inexpensive modality, 3) novel mental state-based visualization techniques, 3D heatmaps, 3D scanpaths, and widget heatmaps that find parts of the user interface where users are perhaps unsure, annoyed, frustrated, or satisfied.
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Saghafi, Abolfazl. "Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6946.

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The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation. To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the design and implementation of automatic biomedical signal pre-processing and monitoring algorithms, the design of novel feature extraction methods, and the design of classification techniques for specific decision making processes. In the first part of this dissertation Electroencephalogram (EEG) signals that are recorded in 14 different locations on the scalp are utilized to detect random eye state change in real-time. In summary, cross channel maximum and minimum is used to monitor real-time EEG signals in 14 channels. Upon detection of a possible change, Multivariate Empirical Mode Decomposes the last two seconds of the signal into narrow-band Intrinsic Mode Functions. Common Spatial Pattern is then employed to create discriminating features for classification purpose. Logistic Regression, Artificial Neural Network, and Support Vector Machine classifiers all could detect the eye state change with 83.4% accuracy in less than two seconds. We could increase the detection accuracy to 88.2% by extracting relevant features from Intrinsic Mode Functions and directly feeding it to the classification algorithms. Our approach takes less than 2 seconds to detect an eye state change which provides a significant improvement and promising real-life applications when compared to slow and computationally intensive instance based classification algorithms proposed in literatures. Increasing the training examples could even improve the accuracy of our analytic algorithms. We employ our proposed analytic method in detecting the three different dance moves that honey bees perform to communicate the location of a food source. The results are significantly better than other alternative methods in the literature in terms of both accuracy and run time. The last chapter of the dissertation brings out a collaborative research on Parkinson's disease. As a Parkinson’s Progression Markers Initiative (PPMI) investigator, I had access to the vast database of The Michael J. Fox Foundation for Parkinson's Research. We utilized available data to study the heredity factors leading to Parkinson's disease by using Maximum Likelihood and Bayesian approach. Through sophisticated modeling, we incorporated information from healthy individuals and those diagnosed with Parkinson's disease (PD) to available historical data on their grandparents' family to draw Bayesian estimations for the chances of developing PD in five types of families. That is, families with negative history of PD (type 1) and families with positive history in which estimations provided for the prevalence of developing PD when none of the parents (type 2), one of the parents (type 3 and 4), or both of the parents (type 5) carried the disease. The results in the provided data shows that for the families with negative history of PD the prevalence is estimated to be 20% meaning that a child in this family has 20% chance of developing Parkinson. If there is positive history of PD in the family the chance increases to 33% when none of the parents had PD and to 44% when both of the parents had the disease. The chance of developing PD in a family whose solely mother is diagnosed with the disease is estimated to be 26% in comparison to 31% when only father is diagnosed with Parkinson's.
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Pearce, Jacqueline Winona. "Detection of Leptospira interrogans in fixed equine eyes affected with end-stage equine recurrent uveitis." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4925.

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Thesis (M.S.)--University of Missouri-Columbia, 2007.
"May 2007" The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Includes bibliographical references.
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Liao, Chi-Hung, and 廖啟宏. "Real-Time Eye Tracking and State Detection Based on Eyeglasses Frame Blocks." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/33867416086799795524.

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碩士
國立臺灣科技大學
電子工程系
96
The role of image tracking today has become much more important for both convenience and security issues. A few of examples using image tracking technique are eye mouse system and driver fatigue detection. In this thesis, our main purpose is to track the location of human eyes and to detect the state of eyes, which can be open or closed. Many researches had investigated the problem of eye tracking. Most of them developed their systems only for naked eye users.(i.e. users without wearing eyeglasses). When a person wears eyeglasses, however, the eyeglasses result in high inaccuracy for eye detection and tracking, mainly from eyeglasses frame and reflection of lens. We propose a method that excludes eyeglasses frame from search area of eyes by using active contour. In the eye detection step, we use eye mask and deformable template to detect eyes within search area, which is determined by active contour. Active contour has good benefit to get edge of frame. It will help us to reduce effect of eyeglasses frame and reflection of lens. We use block matching algorithm on central eyeglasses block for our tracking strategy, taking advantage of the fact that the shape of eyeglasses is fixed. Finally, eye state will be determined after we track the position of eyes. Experiment show that our method can effectively track eyes and detect eye state.
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Abich, Julian. "Investigating the universality and comprehensive ability of measures to assess the state of workload." Doctoral diss., 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6051.

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Measures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all tasks, much less each domain. To date, no single measure, systematically tested across experimental tasks, domains, and other measures is considered a universal measure of workload. Most researchers would argue that multiple measures from various categories should be applied to a given task to comprehensively assess workload. The goal for Study 1 to establish task load manipulations for two theoretically different tasks that induce distinct levels of workload assessed by both subjective and performance measures was successful. The results of the subjective responses support standardization and validation of the tasks and demands of that task for investigating workload. After investigating the use of subjective and objective measures of workload to identify a universal and comprehensive measure or set of measures, based on Study 2, it can only be concluded that not one or a set of measures exists. Arguably, it is not to say that one will never be conceived and developed, but at this time, one does not reside in the psychometric catalog. Instead, it appears that a more suitable approach is to customize a set of workload measures based on the task. The novel approach of assessing the sensitivity and comprehensive ability of conjointly utilizing subjective, performance, and physiological workload measures for theoretically different tasks within the same domain contributes to the theory by laying the foundation for improving methodology for researching workload. The applicable contribution of this project is a stepping-stone towards developing complex profiles of workload for use in closed-loop systems, such as human-robot team interaction. Identifying the best combination of workload measures enables human factors practitioners, trainers, and task designers to improve methodology and evaluation of system designs, training requirements, and personnel selection.
Ph.D.
Doctorate
Industrial Engineering and Management Systems
Engineering and Computer Science
Modeling & Simulation; Engineering
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Chang, Chia-chuan, and 張家銓. "A Driver Fatigue Detection System Based on Eye States Tracking." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03940611027855608541.

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碩士
國立成功大學
工程科學系碩博士班
97
In this thesis, a driver fatigue detection system based on tracking driver’s eye states was implemented. The system contains five parts: face detection, eye position detection, eye tracking, recognize eye state and fatigue detection. Firstly, skin color and projection methods are used to get the face area of an image. In eye position detection, the possible location area of eyes on the face area is selected and then the Sobel vertical operator is used to get the edge image of the selected subimage. Again, projection method is used for the edge image to obtain the eye’s position. Particle filtering method, according to the Gaussian distribution is adopted to perform the eyes tracking of the video frames. Two templates, eyes open and eyes closed are used to estimate the weights of the particles and to decide if the eyes are open or closed. Finally, the rate of eye closed frames within a certain period of time can be used to determine whether the driver fatigues or not. For evaluation the proposed system, 9 videos which taken in the day time with varied brightness were tested. It is shown that the system always gives a correct result.
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Lin, Guo-Wei, and 林國暐. "Intelligent Detection System for the State of Human Eyes." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/96948681755289028491.

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碩士
龍華科技大學
電機工程研究所
98
An intelligent detection system for the state of human eyes is studied and designed in this thesis. This system is used to detect the open or closed state of the human eyes and suitable for the human face images that are under right angle and without wearing spectacles. The thesis is composed of three parts. The first part is constructing human eye state image database. The second part is human face detection and skin-color verification. For human face detection, we use Adaboost human face detection algorithm to find the face, but there are some results that are false positive for the detected result. We use HSV skin-color detection to exclude the false positive results. Besides, we utilize the binary image of skin-color image to search the feature of eyes and to segment the eye area. Then we use the segmentation result to perform 2DPCA algorithm and get the image feature matrix. We compare the image feature matrix of segmented image with the image feature matrices of the eye-state images in the database. According to the minimal distance rule, we can determine the human eyes belonging to which kind of state. Finally, three kinds of 2DPCA algorithms, i.e. 2DPCA, T-2DPCA and2D2DPCA, are used and compared in this thesis. From the testing results, the successful rate of the recognition is nearly 90%. In addition, the comparison results also show that the successful rate of 2DPCA is better than that of T-2DPCA and 2D2DPCA, and the results of detection time show that 2D2DPCA is better than that of 2DPCA and T-2DPCA.
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Books on the topic "Eye state detection"

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Allan Pinkerton: The first private eye. New York: J. Wiley & Sons, 1997.

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A, Mackay James. Allan Pinkerton: The Eye who never slept. Edinburgh: Mainstream, 1996.

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Eye of the raven. Berkeley: Counterpoint, 2009.

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Laurie, Victoria. Crime seen: A psychic eye mystery. New York: Obsidian/New American Library, 2007.

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Banville, John. The black-eyed blonde. [Place of publication not identified]: Macmillan, 2015.

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Johansen, Iris. Close your eyes. Thorndike, Maine: Center Point Publishing, 2012.

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Roy, Johansen, ed. Close your eyes. New York: St. Martin's Press, 2012.

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Marie, Robertson Eleanor. Innocent in Death. New York: Penguin Group USA, Inc., 2008.

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The eye that never sleeps : how Detective Pinkerton saved President Lincoln. Abrams Books for Young Readers, 2018.

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Lagunes, Paul. The Eye and the Whip. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197577622.001.0001.

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Corruption vulnerabilities exist where government officials have power over the provision of goods and the imposition of costs. Building permits and infrastructure contracts are examples of state-issued goods. Traffic tickets and tax liabilities are examples of costs levied by the state. These and other corruption vulnerabilities turn to actual threats when officials calculate that the benefits of abusing their power are greater than the penalties associated with getting caught. By a similar logic, the formula for corruption control requires increasing the probability of detecting corruption (that is, of activating the eye) through enhanced monitoring and then credibly threatening to apply the appropriate penalty in response to wrongdoing (cracking the whip). Notably, the common policy response to corruption often emphasizes only the first of the two mechanisms. Governments prioritize transparency measures but avoid the risks associated with confronting corruption. Therefore, as a means to improve on the current state of affairs, this book examines distinct approaches to promoting accountability, especially accountability among the set of unelected officials responsible for regulating the built environment. It analyzes the results of field experiments on corruption control conducted in the City of Querétaro in central Mexico, urban and peri-urban districts in Peru, and two of New York City’s boroughs. The book contributes evidence-based recommendations for how societies can go about fighting bureaucratic corruption.
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Book chapters on the topic "Eye state detection"

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Tian, Ying-li, Takeo Kanade, and Jeffrey F. Cohn. "Eye-State Action Unit Detection by Gabor Wavelets." In Advances in Multimodal Interfaces — ICMI 2000, 143–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40063-x_19.

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Sun, Rui, and Zheng Ma. "Robust and Efficient Eye Location and Its State Detection." In Advances in Computation and Intelligence, 318–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04843-2_34.

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Söylemez, Ömer Faruk, and Burhan Ergen. "Eye Location and Eye State Detection in Facial Images Using Circular Hough Transform." In Computer Information Systems and Industrial Management, 141–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40925-7_14.

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Huang, Bin, Renwen Chen, Wang Xu, Qinbang Zhou, and Xu Wang. "Improved Fatigue Detection Using Eye State Recognition with HOG-LBP." In Proceedings of the 9th International Conference on Computer Engineering and Networks, 365–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3753-0_35.

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Xu, Xinzheng, Xiaoming Cui, Guanying Wang, Tongfeng Sun, and Hongguo Feng. "A New Method for Driver Fatigue Detection Based on Eye State." In Rough Sets and Knowledge Technology, 513–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25754-9_45.

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Punitha, A., and M. Kalaiselvi Geetha. "Driver Eye State Detection Based on Minimum Intensity Projection Using Tree Based Classifiers." In Advances in Intelligent Systems and Computing, 103–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23036-8_9.

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Jo, Hyunrae, and Minho Lee. "In-attention State Monitoring for a Driver Based on Head Pose and Eye Blinking Detection Using One Class Support Vector Machine." In Neural Information Processing, 110–17. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_14.

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Forczmański, Paweł, and Anton Smoliński. "Eyes State Detection in Thermal Imaging." In Image Processing and Communications, 22–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_4.

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Wang, Jing-Wein, and Chin-Chun Kuo. "A Robust Two Stage Approach for Eye Detection." In Image Analysis and Processing – ICIAP 2005, 431–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_53.

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Tian, Yuexin, Changyuan Wang, and Hongbo Jia. "Eyes and Mouth States Detection for Drowsiness Determination." In Lecture Notes in Electrical Engineering, 1546–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_200.

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Conference papers on the topic "Eye state detection"

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Sathyanarayana, Supriya, Ravi Kumar Satzoda, Srikanthan Thambipillai, and Suchitra Sathyanarayana. "Compute-efficient eye state detection." In ICDSC '15: International Conference on distributed Smart Cameras. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2789116.2789144.

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Fuangkaew, Supakit, and Karn Patanukhom. "Eye State Detection and Eye Sequence Classification for Paralyzed Patient Interaction." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.91.

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Mali, Hemantkumar B., and S. D. Lokhande. "Eye state detection using center of gravity approach." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030611.

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Bolosan, Jenel Luise C., Mary Lisette L. dela Torre, Josephine R. Gomez, John Albert S. Luna, Mari Fatima P. Serrano, Seigfred V. Prado, Celdrian Rei B. Asilo, et al. "Eye state analysis using EyeMap for drowsiness detection." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7372984.

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Wu, Yu-Shan, Ting-Wei Lee, Quen-Zong Wu, and Heng-Sung Liu. "An Eye State Recognition Method for Drowsiness Detection." In 2010 IEEE 71st Vehicular Technology Conference. IEEE, 2010. http://dx.doi.org/10.1109/vetecs.2010.5493951.

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Zhang, Fang, Jingjing Su, Lei Geng, and Zhitao Xiao. "Driver Fatigue Detection Based on Eye State Recognition." In 2017 International Conference on Machine Vision and Information Technology (CMVIT). IEEE, 2017. http://dx.doi.org/10.1109/cmvit.2017.25.

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Du, Yong, Peijun Ma, Xiaohong Su, and Yingjun Zhang. "Driver Fatigue Detection based on Eye State Analysis." In 11th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2008. http://dx.doi.org/10.2991/jcis.2008.23.

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Liu, Zhen-Tao, Si-Han Li, Cheng-Shan Jiang, Dan-Yun Li, and Man Hao. "A Novel Eye State Detection Method via WBCNN." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9188388.

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Liu, Hong, Yuwen Wu, and Hongbin Zha. "Eye state detection from color facial image sequence." In Second International Conference on Image and Graphics, edited by Wei Sui. SPIE, 2002. http://dx.doi.org/10.1117/12.477054.

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Wang, Feng, Mi Zhou, and Bingchu Zhu. "A novel feature based rapid eye state detection method." In 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.5420853.

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