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Journal articles on the topic 'Eye state detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

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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2

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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3

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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4

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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8

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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9

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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10

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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11

Wisesty, Untari Novia. "Levenberg-Marquardt Neural Network for Eye States Detection Based on Electroencephalography Data." International Journal on Information and Communication Technology (IJoICT) 2, no. 1 (July 1, 2016): 23. http://dx.doi.org/10.21108/ijoict.2016.21.72.

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The eye state detection is one of various task toward Brain Computer Interface system. The eye state can be read in brain signals. In this paper use EEG Eye State dataset (Rosler, 2013) from UCI Machine Learning Repository Database. Dataset is consisting of continuous 14 EEG measurements in 117 seconds. The eye states were marked as “1” or “0”. “1” indicates the eye-closed and “0” the eye-open state. The proposed schemes use Multi Layer Neural Network with Levenberg Marquardt optimization learning algorithm, as classification method. Levenberg Marquardt method used to optimize the learning algorithm of neural network, because the standard algorithm has a weak convergence rate. It is need many iterations to have minimum error. Based on the analysis towards the experiment on the EEG dataset, it can be concluded that the proposed scheme can be implemented to detect the Eye State. The best accuracy gained from combination variable sigmoid function, data normalization and number of neurons are 31 (95.71%) for one hidden layer, and 98.912% for two hidden layers with number of neurons are 39 and 47 neurons and linear function.

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12

Laport, Francisco, Paula M. Castro, Adriana Dapena, Francisco J. Vazquez-Araujo, and Daniel Iglesia. "Study of Machine Learning Techniques for EEG Eye State Detection." Proceedings 54, no. 1 (August 31, 2020): 53. http://dx.doi.org/10.3390/proceedings2020054053.

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A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.

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Eddine, Benrachou Djamel, Filipe Neves dos Santos, Brahim Boulebtateche, and Salah Bensaoula. "EyeLSD a Robust Approach for Eye Localization and State Detection." Journal of Signal Processing Systems 90, no. 1 (January 31, 2017): 99–125. http://dx.doi.org/10.1007/s11265-016-1219-1.

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M, Kavitha, Mohamed Mansoor Roomi S, K. Priya, and Bavithra Devi K. "State model based face mask detection." International Journal of Engineering & Technology 7, no. 2.22 (April 20, 2018): 35. http://dx.doi.org/10.14419/ijet.v7i2.22.11805.

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The Automatic Teller Machine plays an important role in the modern economic society. ATM centers are located in remote central which are at high risk due to the increasing crime rate and robbery.These ATM centers assist with surveillance techniques to provide protection. Even after installing the surveillance mechanism, the robbers fool the security system by hiding their face using mask/helmet. Henceforth, an automatic mask detection algorithm is required to, alert when the ATM is at risk. In this work, the Gaussian Mixture Model (GMM) is applied for foreground detection to extract the regions of interest (ROI) i.e. Human being. Face region is acquired from the foreground region through the torso partitioning and applying Viola-Jones algorithm in this search space. Parts of the face such as Eye pair, Nose, and Mouth are extracted and a state model is developed to detect mask.

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15

Al-Rahayfeh, Amer, and Miad Faezipour. "Eye Tracking and Head Movement Detection: A State-of-Art Survey." IEEE Journal of Translational Engineering in Health and Medicine 1 (2013): 2100212. http://dx.doi.org/10.1109/jtehm.2013.2289879.

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Ji, Yingyu, Shigang Wang, Yang Lu, Jian Wei, and Yan Zhao. "Eye and mouth state detection algorithm based on contour feature extraction." Journal of Electronic Imaging 27, no. 05 (February 9, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.5.051205.

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17

Saghafi, Abolfazl, Chris P. Tsokos, Mahdi Goudarzi, and Hamidreza Farhidzadeh. "Random eye state change detection in real-time using EEG signals." Expert Systems with Applications 72 (April 2017): 42–48. http://dx.doi.org/10.1016/j.eswa.2016.12.010.

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18

González-Ortega, D., F. J. Díaz-Pernas, M. Antón-Rodríguez, M. Martínez-Zarzuela, and J. F. Díez-Higuera. "Real-time vision-based eye state detection for driver alertness monitoring." Pattern Analysis and Applications 16, no. 3 (April 23, 2013): 285–306. http://dx.doi.org/10.1007/s10044-013-0331-0.

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19

Hayati, Pedram, and Vidyasagar Potdar. "Spam 2.0 State of the Art." International Journal of Digital Crime and Forensics 4, no. 1 (January 2012): 17–36. http://dx.doi.org/10.4018/jdcf.2012010102.

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Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content, or a manipulated Wiki page are examples of Spam 2.0. In this paper, the authors provide a comprehensive survey of the state-of-the-art, detection-based, prevention-based and early-detection-based Spam 2.0 filtering methods.

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20

Sudhakar, J., and S. Srinivasan. "Driver’s Drowsiness Behaviour Detection by Using PSO/DPSO Algorithm for Urban Road System." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 516. http://dx.doi.org/10.14419/ijet.v7i3.27.18474.

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In recent years driver fatigue is one of the major causes for vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver drowsiness. So it is very important to detect the drowsiness of the driver to save life and property. In our system, this aims to develop 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 each closure value of eye is considered. So when the closure of eye exceeds a certain amount then the driver is identified to be sleepy. The entire system is implemented using PSO, DPSO and FODPSO algorithm and detection of drowsiness behaviour of driver different eye state level.

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21

Yang, Hai Yan, Xin Hua Jiang, Lei Wang, and Yong Hui Zhang. "Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM." Applied Mechanics and Materials 128-129 (October 2011): 123–29. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.123.

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Eye statement is one of the most important factors reflecting driver fatigue. A novel eye statement recognition method for driver fatigue detection based on Gabor transformation and Hidden Markov Model is proposed in this paper, in which, the eye detection algorithm is borrowed from Zafer Savas' TrackEye software, and Gabor features, i.e. the eye state features, of the eye are extracted by using Gabor wavelet. After that, by using these features, the classifier is trained by HMM (Hidden Markov Model) to distinguish the eye states including fatigue and alert, then the consecutive five frames are considered to judge whether there exists driver fatigue or not. Simulation results show that the new method has good accuracy and effectiveness.

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Sathish, K., Kumar Sanu Raj, J. V. Adithya Chowdary, and Nitish Jahagirdar. "Machine Learning Approach to Detect Red-Eye Using Pixel Detection Technique." Journal of Computational and Theoretical Nanoscience 17, no. 4 (April 1, 2020): 1692–95. http://dx.doi.org/10.1166/jctn.2020.8426.

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Sometimes in Flash Photography red colored patches occurred in human eyes. It is actually a reflection of bright flash light reflected from blood vessels in the eyes, giving the eye an unnatural red hue. Red-eye is a big problem in professional photography. Most red-eye reduction systems in many editing software needed the user to identify the red-eye and make an outline through the red-eye. Here we propose an Automatic Red-Eye Detection System instead. The system contains a red-eye detector that finds bunch of red pixels those are clustered to gather, a state of face detector that used to eliminate most false positives (pixel clusters that look red eyes but are not); and a redeye outline detector. All three detectors are automatically learned from the taken datasets and with a proper classifiers using boosting. For creating a fully Automatic Red-Eye Corrector this system needed to be combined with a functional Red-Eye Reduction model.

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23

Gurve, Dharmendra, and Sridhar Krishnan. "Deep Learning of EEG Time–Frequency Representations for Identifying Eye States." Advances in Data Science and Adaptive Analysis 10, no. 02 (April 2018): 1840006. http://dx.doi.org/10.1142/s2424922x18400065.

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A new Convolutional Neural Network (CNN) architecture to classify nonstationary biomedical signals using their time–frequency representations is proposed. The present method uses the spectrogram of the biomedical signals as an input to CNN, in addition Non-negative matrix factorization (NMF) dictionary elements are used as an additional feature to improve the performance of the CNN model. Considering a number of applications involving eye state classification, such as in Parkinson’s disease detection, analysis of eye fatigue in 3D TVs, driver’s drowsiness detection, infant sleep-waking state identification, and classification of bipolar mood disorder and attention deficit hyperactivity, the proposed method was applied to Electroencephalography (EEG) data for classification of eye state. First, the spectrogram of EEG signal is obtained and used as an image input to CNN, simultaneously, the NMF feature is also fed to CNN. Further, both features are combined in fully connected layer of CNN architecture. The proposed method is compared with other existing methods for eye state detection and shows good classification accuracy with 96.16%. The prediction rate for the proposed method is 134 observations/second, which is suitable for brain–computer interface applications.

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Yang, Yang, Chao-Ying Gao, and Dewen Dong. "Tetraphenylethene functionalized rhodamine dye for fluorescence detection of HCl vapor in the solid state." Analytical Methods 8, no. 44 (2016): 7898–902. http://dx.doi.org/10.1039/c6ay01582d.

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Li Jianping, 李建平, 牛燕雄 Niu Yanxiong, 杨露 Yang Lu, 张颖 Zhang Ying, and 吕建明 Lü Jianming. "Contactless Driver Fatigue Detection and Warning System Based on Eye State Information." Laser & Optoelectronics Progress 52, no. 4 (2015): 041101. http://dx.doi.org/10.3788/lop52.041101.

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26

Mori, Taizo, Masaaki Akamatsu, Ken Okamoto, Masato Sumita, Yoshitaka Tateyama, Hideki Sakai, Jonathan P. Hill, Masahiko Abe, and Katsuhiko Ariga. "Micrometer-level naked-eye detection of caesium particulates in the solid state." Science and Technology of Advanced Materials 14, no. 1 (February 7, 2013): 015002. http://dx.doi.org/10.1088/1468-6996/14/1/015002.

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27

Lai, Chi Qin, Haidi Ibrahim, Aini Ismafairus Abd. Hamid, Mohd Zaid Abdullah, Azlinda Azman, and Jafri Malin Abdullah. "Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography." Computational Intelligence and Neuroscience 2020 (March 11, 2020): 1–10. http://dx.doi.org/10.1155/2020/8923906.

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Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

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Alsaeedi, Nassr, and Dieter Wloka. "Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)." Sensors 19, no. 5 (March 5, 2019): 1121. http://dx.doi.org/10.3390/s19051121.

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The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies.

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Wei, Lingxiang, Tianliu Feng, Pengfei Zhao, and Mingjun Liao. "Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine." Baltic Journal of Road and Bridge Engineering 16, no. 1 (March 29, 2021): 118–39. http://dx.doi.org/10.7250/bjrbe.2021-16.518.

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Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.

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Chen, Liang Hwa, Po Lun Chang, Guo Wei Lin, and Yen Ching Chang. "Intelligent Human Eye State Identification Based on 2DPCA and Skin Color." Applied Mechanics and Materials 145 (December 2011): 252–56. http://dx.doi.org/10.4028/www.scientific.net/amm.145.252.

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Human eye state identification can be applied not only to monitoring of the drowsiness of a human car driver but also to medical treatment facilitating system for monitoring neonate or stuporous patient. Once the patient awake and open his eyes, human eye state identification system can notify nurses to take care of the patient. In this work, we propose an intelligent human eye state identification algorithm based on 2DPCA and skin color. Adaboost face detection function of OpenCV is first adopted to detect the human faces in color images acquired from camera. Then, we develop a more precise HSV skin color model and use it to eliminate the false alarms in the previous stage. Next, a heuristic segmentation method based on skin color and face geometry is proposed to segment the region of eyes, from which 2DPCA is then adopted to extract the features and identify the opening or closing state of eyes. We study three kinds of 2DPCA, i.e. 2DPCA, T-2DPCA and (2D)2PCA, and compare their performance. Experimental results reveal that our algorithm can achieve over 90% accuracy rate.

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Chinsatit, Warapon, and Takeshi Saitoh. "CNN-Based Pupil Center Detection for Wearable Gaze Estimation System." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8718956.

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This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user’s eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus, estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify the eye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows a closed eye. In addition, this paper presents a process to create an eye image dataset using a wearable camera. This dataset, which was used to evaluate the proposed method, has approximately 20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation.

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Li, Ya Li, Bin Hu, Sheng Jin Wang, and Xiao Qing Ding. "Real-Time Eye Locating and Tracking for Driver Fatigue Detection." Applied Mechanics and Materials 20-23 (January 2010): 1359–64. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1359.

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Assistant driving systems have attracted more and more attention during recent years. Among them fatigue detection plays a key role because of its close relationship with accidents. In this paper, we propose a novel method which uses eye locating and tracking technique to detect driver fatigue. The present method consists of four steps. First, we employ Adaboost and Haar-like features to construct a robust classifier which can detect eye corner points. Second, we use extended parabolic Hough transformation to construct the parabola curves of upper and lower eyelid. Then, particle filter algorithm is used to track eye corner points in video sequences. Finally, the driver fatigue state is estimated through computing the frequency of eye opening and closing intervals. Experimental results from real environment datasets are given in our discussion as well.

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Langstrand, Jens-Patrick, Hoa T. Nguyen, and Michael Hildebrandt. "Synopticon: Sensor Fusion for Real-Time Gaze Detection and Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 311–15. http://dx.doi.org/10.1177/1541931218621072.

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Synopticon is a software platform that fuses data from position tracking, eye tracking, and physiological sensors. Synopticon was developed to produce real-time digital representations of users. These “digital twins” can be visualized, or used by other algorithms to detect the behavioural, cognitive or emotional state of the user. Synopticon provides 3D modelling tools based on position tracking data to define areas of interest (AOI) in the environment. By projecting the combined eye-and position-data into the 3D model, Synopticon can automatically detect when a user is looking at an AOI, generates real-time heat maps, and compiles statistical information. The demonstration will show how to set up and calibrate a combined position tracking and eye tracking system, and explain how Synopticon addresses some of the limitations of current eye tracking technology.

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Zandi, Ali Shahidi, Azhar Quddus, Laura Prest, and Felix J. E. Comeau. "Non-Intrusive Detection of Drowsy Driving Based on Eye Tracking Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 16, 2019): 247–57. http://dx.doi.org/10.1177/0361198119847985.

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Drowsy driving is one of the leading causes of motor vehicle accidents in North America. This paper presents the use of eye tracking data as a non-intrusive measure of driver behavior for detection of drowsiness. Eye tracking data were acquired from 53 subjects in a simulated driving experiment, whereas the simultaneously recorded multichannel electroencephalogram (EEG) signals were used as the baseline. A random forest (RF) and a non-linear support vector machine (SVM) were employed for binary classification of the state of vigilance. Different lengths of eye tracking epoch were selected for feature extraction, and the performance of each classifier was investigated for every epoch length. Results revealed a high accuracy for the RF classifier in the range of 88.37% to 91.18% across all epoch lengths, outperforming the SVM with 77.12% to 82.62% accuracy. A feature analysis approach was presented and top eye tracking features for drowsiness detection were identified. Altogether, this study showed a high correspondence between the extracted eye tracking features and EEG as a physiological measure of vigilance and verified the potential of these features along with a proper classification technique, such as the RF, for non-intrusive long-term assessment of drowsiness in drivers. This research would ultimately lead to development of technologies for real-time assessment of the state of vigilance, providing early warning of fatigue and drowsiness in drivers.

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Wang, Qin, Lan Tang, and Kun Yang. "Driver Fatigue Detection Algorithm Research Based on the Characteristics of Eyes." Applied Mechanics and Materials 701-702 (December 2014): 30–35. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.30.

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In driver fatigue warning system, it is a very effective method for detecting Driver fatigue state through the driver's facial expressions and body movements. The main content of this article is to detect the two basic states of the eyes opening and closing and presents the LBP texture detection operator. Firstly we get the face image sequences using infrared video and extract the eye region using ADABOOST. The SVM is used in classifying feature vector of the eyes open and closed detecting of driver fatigue. A large number of experimental results show that the proposed method has high detection accuracy and timeliness.

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Tiawongsombat, Prasertsak, and Choopan Rattanapoka. "A Study of Two Robust Features for Effective Open or Closed Eye Classification." Applied Mechanics and Materials 781 (August 2015): 507–10. http://dx.doi.org/10.4028/www.scientific.net/amm.781.507.

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Open and closed states of eyes play an important role in human-computer interaction. They can be used as communication method for people with severe disabilities providing an alternate input modality to control a computer or as detection method for a driver's drowsiness. This paper introduces a study on two eye components (i.e., iris and sclera) for robust open or closed eye classification. Evidently, the area of iris or sclera increases while a person opens an eye and decreases while an eye is closing. In particular, the distributions of these eye components, during each eye state, form a bell-like shape. Consequently, an eye state classification can be effectively achieved by Bayes classifier. Finally, the performance comparison of the proposed features against the ground truth is discussed.

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Liu, Zhen-Tao, Cheng-Shan Jiang, Si-Han Li, Min Wu, Wei-Hua Cao, and Man Hao. "Eye state detection based on Weight Binarization Convolution Neural Network and Transfer Learning." Applied Soft Computing 109 (September 2021): 107565. http://dx.doi.org/10.1016/j.asoc.2021.107565.

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Xiao, Can, Xiaofang Zhang, Junfeng Liu, Ankang Yang, Hong Zhao, Xiangjun Li, Yujian He, and Zhuobin Yuan. "Sensitive colorimetric detection of melamine with 1,4-dithiothreitol modified gold nanoparticles." Analytical Methods 7, no. 3 (2015): 924–29. http://dx.doi.org/10.1039/c4ay02491e.

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The presence of melamine induces a change in the state of 1,4-dithiothreitol modified gold nanoparticles, and the concentration of melamine could be quantified by the naked eye or using a UV-vis spectrometer.

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White, Alex L., and Martin Rolfs. "Oculomotor inhibition covaries with conscious detection." Journal of Neurophysiology 116, no. 3 (September 1, 2016): 1507–21. http://dx.doi.org/10.1152/jn.00268.2016.

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Saccadic eye movements occur frequently even during attempted fixation, but they halt momentarily when a new stimulus appears. Here, we demonstrate that this rapid, involuntary “oculomotor freezing” reflex is yoked to fluctuations in explicit visual perception. Human observers reported the presence or absence of a brief visual stimulus while we recorded microsaccades, small spontaneous eye movements. We found that microsaccades were reflexively inhibited if and only if the observer reported seeing the stimulus, even when none was present. By applying a novel Bayesian classification technique to patterns of microsaccades on individual trials, we were able to decode the reported state of perception more accurately than the state of the stimulus (present vs. absent). Moreover, explicit perceptual sensitivity and the oculomotor reflex were both susceptible to orientation-specific adaptation. The adaptation effects suggest that the freezing reflex is mediated by signals processed in the visual cortex before reaching oculomotor control centers rather than relying on a direct subcortical route, as some previous research has suggested. We conclude that the reflexive inhibition of microsaccades immediately and inadvertently reveals when the observer becomes aware of a change in the environment. By providing an objective measure of conscious perceptual detection that does not require explicit reports, this finding opens doors to clinical applications and further investigations of perceptual awareness.

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Bellet, Marie E., Joachim Bellet, Hendrikje Nienborg, Ziad M. Hafed, and Philipp Berens. "Human-level saccade detection performance using deep neural networks." Journal of Neurophysiology 121, no. 2 (February 1, 2019): 646–61. http://dx.doi.org/10.1152/jn.00601.2018.

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Saccades are ballistic eye movements that rapidly shift gaze from one location of visual space to another. Detecting saccades in eye movement recordings is important not only for studying the neural mechanisms underlying sensory, motor, and cognitive processes, but also as a clinical and diagnostic tool. However, automatically detecting saccades can be difficult, particularly when such saccades are generated in coordination with other tracking eye movements, like smooth pursuits, or when the saccade amplitude is close to eye tracker noise levels, like with microsaccades. In such cases, labeling by human experts is required, but this is a tedious task prone to variability and error. We developed a convolutional neural network to automatically detect saccades at human-level accuracy and with minimal training examples. Our algorithm surpasses state of the art according to common performance metrics and could facilitate studies of neurophysiological processes underlying saccade generation and visual processing. NEW & NOTEWORTHY Detecting saccades in eye movement recordings can be a difficult task, but it is a necessary first step in many applications. We present a convolutional neural network that can automatically identify saccades with human-level accuracy and with minimal training examples. We show that our algorithm performs better than other available algorithms, by comparing performance on a wide range of data sets. We offer an open-source implementation of the algorithm as well as a web service.

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Duan, Jing Li, Chun Fei Zhang, and Qiu Shuang Wang. "Research of Fatigue Driving Warning System Based on Android." Applied Mechanics and Materials 701-702 (December 2014): 400–404. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.400.

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This paper proposed a method for detecting fatigue detection Android smart phone system, and applied to the Android system. The system monitors the state of fatigue by smart phones photographs. Face detection method is used to localize the eyes of the driver and the eye region is extracted to monitor the movement of eyelids. An alarm rule is designed based on the PERCLOS standard to detect drowsy driving.Experiments show that the method is accurate rate, running speed, and can be used to monitor driver fatigue during the day.

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Ghosh, Kumaresh, Santanu Panja, and Subratanu Bhattacharya. "Naphthalene linked pyridyl urea as a supramolecular gelator: a new insight into naked eye detection of I−in the gel state with semiconducting behaviour." RSC Advances 5, no. 89 (2015): 72772–79. http://dx.doi.org/10.1039/c5ra11721f.

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Chirra, Venkata, Srinivasulu ReddyUyyala, and Venkata KishoreKolli. "Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State." Revue d'Intelligence Artificielle 33, no. 6 (December 30, 2019): 461–66. http://dx.doi.org/10.18280/ria.330609.

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Li, Zhanxian, Xingjiang Liu, Wanying Zhao, Sheng Wang, Wan Zhou, Liuhe Wei, and Mingming Yu. "Naked-Eye Detection of C1–C4 Alcohols Based on Ground-State Intramolecular Proton Transfer." Analytical Chemistry 86, no. 5 (February 13, 2014): 2521–25. http://dx.doi.org/10.1021/ac403550t.

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Mandal, Bappaditya, Liyuan Li, Gang Sam Wang, and Jie Lin. "Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State." IEEE Transactions on Intelligent Transportation Systems 18, no. 3 (March 2017): 545–57. http://dx.doi.org/10.1109/tits.2016.2582900.

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Wu, Cuiyan, Hai Xu, Yaqian Li, Ruihua Xie, Peijuan Li, Xiao Pang, Zile Zhou, Haitao Li, and Youyu Zhang. "A ‘‘naked-eye’’ colorimetric and ratiometric fluorescence probe for trace hydrazine." Analytical Methods 11, no. 19 (2019): 2591–96. http://dx.doi.org/10.1039/c9ay00535h.

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A new colorimetric and ratiometric fluorescence probe was developed for detection of hydrazine (N₂H₄) based on the mechanism of intramolecular proton transfer excited state (ESIPT).

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Chen, Peng, Guo-Jie Liu, Yuyang Wang, and Sean Xiao-An Zhang. "A stable aggregate system of silyl ether substituted quinacridone and its aggregation-state changes induced by fluoride-ions: inspiration for a dual guaranteed strategy for probe design." RSC Advances 6, no. 31 (2016): 25986–91. http://dx.doi.org/10.1039/c6ra01487a.

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Shin, Su-Jin, Seyeob Kim, Youngjung Kim, and Sungho Kim. "Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images." Remote Sensing 12, no. 17 (August 24, 2020): 2734. http://dx.doi.org/10.3390/rs12172734.

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Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always guaranteed to be labeled up to fine categories. We propose a hierarchical multi-label object detection framework applicable to hierarchically partial-annotated datasets. In the framework, an object detection pipeline called Decoupled Hierarchical Classification Refinement (DHCR) fuses the results of two networks: (1) an object detection network with multiple classifiers, and (2) a hierarchical sibling classification network for supporting hierarchical multi-label classification. Our framework additionally introduces a region proposal method for efficient detection on vain areas of the remote sensing images, called clustering-guided cropping strategy. Thorough experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from WorldView-3 and SkySat satellites. Under our proposed framework, DHCR-based detections significantly improve the performance of respective baseline models and we achieve state-of-the-art results on the datasets.

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Sayyad, Sahil. "A Vision-based Driver Night Time Assistance and Surveillance System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3662–70. http://dx.doi.org/10.22214/ijraset.2021.35878.

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In India round 1.5 lakh humans died per year in avenue twist of fate because of road accidents most of them were due to low vision and weariness problems. Weariness (Extreme Tiredness) or Fatigue is a major purpose of avenue accidents and has extensive implications for street safety. several deadly accidents can be avoided if the drowsy drivers are warned in time. In many cases it is observed that a car hits some object/obstacle on road due to low vision in that case an object detection and warning system can help to avoid such accidents. Basically, Weariness is a state of sleepiness which abnormally happen when we are very tired or whilst drunken. A spread of drowsiness detection strategies exist that monitor the driving force’s drowsiness state at the same time as driving and alarm the drivers if they're no longer concentrating on driving. The relevant features can be extracted from facial expressions including yawning, eye closure and head actions for inferring the level of weariness. The organic condition of driver’s body is analyzed for driver weariness detection. So, this utility overcomes the trouble of driver weariness detection and object/obstacle detection & warning whilst driving using eye extraction, facial extraction, object and its distance detection using different algorithms.

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Li, Bin, and Hong Fu. "Real Time Eye Detector with Cascaded Convolutional Neural Networks." Applied Computational Intelligence and Soft Computing 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/1439312.

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An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.

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