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Journal articles on the topic 'Blink detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

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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2

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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3

Zhang, Jinhua, Baozeng Wang, Cheng Zhang, and Jun Hong. "Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4069790.

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The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.

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4

Kouda, Takaharu. "Detection of Blink and Facial Expression Changes using DCT Signs." Journal of the Institute of Industrial Applications Engineers 2, no. 2 (April 25, 2014): 70–73. http://dx.doi.org/10.12792/jiiae.2.70.

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5

Rogalska, Anna, Filip Rynkiewicz, Marcin Daszuta, Krzysztof Guzek, and Piotr Napieralski. "Blinking Extraction in Eye gaze System for Stereoscopy Movies." Open Physics 17, no. 1 (September 21, 2019): 512–18. http://dx.doi.org/10.1515/phys-2019-0053.

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Abstract The aim of this paper is to present methods for human eye blink recognition. The main function of blinking is to spread tears across the eye and remove irratants from the surface of the cornea and conjuctiva. Blinking can be associated with internal memory processing, fatigue or activation in central nervous system. There are currently many methods for automatic blink detection. The most reliable methods include EOG or EEG signals. These methods, however, are associated with a decrease in the comfort of the examined person. This paper presents a method to detect blinks with the eye-tracker device. There are currently many blink detection methods for this devices. Two popular eye-trackers were tested in this paper. In addition a method for improving detection efficiency was proposed.

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6

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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7

Chen, Xue Jun, and Chen Hua Zhang. "Removing Blinks in Video-Oculography." Applied Mechanics and Materials 239-240 (December 2012): 1165–68. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1165.

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Video-oculography (VOG) is a non-invasive detection method used for eye movement. However, during testing, if object blinks, VOG would be difficult to acquire eye movement. A removing blink method based on Kalman Filter was presented. A cubic spline was employed to patch the removed data. Then simulation and experiment were done. The experimental results show that the method well predicts the next state. Compared to a threshold level, it eliminates blink artifact and patches the removed data. The method is a viable means of predicting pupil center for blink in VOG.

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Liu, Jialin, Dong Li, Lei Wang, and Jie Xiong. "BlinkListener." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 2 (June 23, 2021): 1–27. http://dx.doi.org/10.1145/3463521.

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Eye blink detection plays a key role in many real-life applications such as Human-Computer Interaction (HCI), drowsy driving prevention and eye disease detection. Although traditional camera-based techniques are promising, multiple issues hinder their wide adoption including the privacy concern, strict lighting condition and line-of-sight (LoS) requirements. On the other hand, wireless sensing without a need for dedicated sensors gains a tremendous amount of attention in recent years. Among the wireless signals utilized for sensing, acoustic signals show a unique potential for fine-grained sensing owing to their low propagation speed in the air. Another trend favoring acoustic sensing is the wide availability of speakers and microphones in commodity devices. Promising progress has been achieved in fine-grained human motion sensing such as breathing using acoustic signals. However, it is still very challenging to employ acoustic signals for eye blink detection due to the unique characteristics of eye blink (i.e., subtle, sparse and aperiodic) and severe interference (i.e., from the human target himself and surrounding objects). We find that even the very subtle involuntary head movement induced by breathing can severely interfere with eye blink detection. In this work, for the first time, we propose a system called BlinkListener to sense the subtle eye blink motion using acoustic signals in a contact-free manner. We first quantitatively model the relationship between signal variation and the subtle movements caused by eye blink and interference. Then, we propose a novel method that exploits the "harmful" interference to maximize the subtle signal variation induced by eye blinks. We implement BlinkListener on both a research-purpose platform (Bela) and a commodity smartphone (iPhone 5c). Experiment results show that BlinkListener can achieve robust performance with a median detection accuracy of 95%. Our system can achieve high accuracies when the smartphone is held in hand, the target wears glasses/sunglasses and in the presence of strong interference with people moving around.

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Kumari B.M, Kusuma, Sampada Sethi, Ramakanth Kumar P, Nishant Kumar, and Atulit Shankar. "Detection of Driver Drowsiness using Eye Blink Sensor." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 498. http://dx.doi.org/10.14419/ijet.v7i3.12.16167.

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Accidents due to driver drowsiness can be prevented using eye blink sensors. The driver is supposed to wear the eye blink sensor frame throughout the course of driving and blink has to be for a couple of seconds to detect drowsiness. Any random changes in steering movement leads to reduction in wheel speed. The threshold of the vibration sensor can be varied and accordingly action can be taken. The outcome is that the vibrator attached to eye blink sensor’s frame vibrates if the driver falls asleep and also the LCD displays the warning messages. The wheel is slowed or stopped depending on the condition. This is accompanied by the owner being notified through the GSM module, so the owner can retrieve the driver’s location, photograph and police station list near to driver’s location. This is how the driver can be alerted during drowsiness and the owner can be notified simultaneously

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10

K.Galab, Mai, H. M. Abdalkader, and Hala H. Zayed. "Adaptive Real Time Eye-Blink Detection System." International Journal of Computer Applications 99, no. 5 (August 20, 2014): 29–36. http://dx.doi.org/10.5120/17372-7910.

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11

Morris, T. "Blink detection for real-time eye tracking." Journal of Network and Computer Applications 25, no. 2 (April 2002): 129–43. http://dx.doi.org/10.1016/s1084-8045(02)90130-x.

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12

Lee, Won Oh, Eui Chul Lee, and Kang Ryoung Park. "Blink detection robust to various facial poses." Journal of Neuroscience Methods 193, no. 2 (November 2010): 356–72. http://dx.doi.org/10.1016/j.jneumeth.2010.08.034.

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13

Nakanishi, Masaki, Yasue Mitsukura, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung. "Online Voluntary Eye Blink Detection using Electrooculogram." IEICE Proceeding Series 1 (March 17, 2014): 114–17. http://dx.doi.org/10.15248/proc.1.114.

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14

Morris, T., P. Blenkhorn, and Farhan Zaidi. "Blink detection for real-time eye tracking." Journal of Network and Computer Applications 25, no. 2 (April 2002): 129–43. http://dx.doi.org/10.1006/jnca.2002.0130.

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15

Mukojima, Hiroki, Nozomi Nagamine, and Takuya Nomura. "Blink Detection Method for Obstruction Warning Signals." IEEJ Transactions on Industry Applications 141, no. 3 (March 1, 2021): 212–22. http://dx.doi.org/10.1541/ieejias.141.212.

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16

Viljoen, S., T. Hanekom, and P. J. Cilliers. "Eye-blink controlled computer mouse: design and evaluation." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 23, no. 1/2 (September 23, 2004): 7–12. http://dx.doi.org/10.4102/satnt.v23i1/2.187.

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Physically disabled people who do not have the use of their limbs have difficulty operating a computer, since they cannot use a mouse. In this article the design, implementation and evaluation of an eye-blink controlled computer mouse to be used by handicapped people are described. Detection of voluntary blinks is established by the reflection of infrared light from the skin on the side of the eye, while involuntary blinks are ignored. This enables people who do not have the use of their limbs to operate a computer. All the functions of a PS2 mouse are emulated.

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17

Nguyen, Thanh-Vinh, and Masaaki Ichiki. "Mask-Type Sensor for Pulse Wave and Respiration Measurements and Eye Blink Detection." Sensors 21, no. 14 (July 19, 2021): 4895. http://dx.doi.org/10.3390/s21144895.

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This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor.

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18

Stankevich, Lev A., Sabina S. Amanbaeva, and Aleksandr V. Samochadin. "User Authentication by Electroencephalographic Signals when Blinkin." Computer tools in education, no. 3 (September 30, 2019): 52–69. http://dx.doi.org/10.32603/2071-2340-2019-3-52-69.

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The article presents the results of a study in the field of applying electroencephalography (EEG) for human authentication. An algorithm for EEG authentication based on blinks has been developed and described. Authentication is carried out by one blink, which takes 2-5 seconds. The data is collected using a Muse electroencephalograph. Data preprocessing includes wavelet transform and blink detection. Geometric characteristics of the EEG signals are used as features. Recognition is conducted by the Random Forest classifier. According to the test results, the percentage of correct authentication was 95 %. There is the possibility of background authentication. The implemented system may be used to authenticate students at distant education.

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19

Hoppe, David, Stefan Helfmann, and Constantin A. Rothkopf. "Humans quickly learn to blink strategically in response to environmental task demands." Proceedings of the National Academy of Sciences 115, no. 9 (February 14, 2018): 2246–51. http://dx.doi.org/10.1073/pnas.1714220115.

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Eye blinking is one of the most frequent human actions. The control of blinking is thought to reflect complex interactions between maintaining clear and healthy vision and influences tied to central dopaminergic functions including cognitive states, psychological factors, and medical conditions. The most imminent consequence of blinking is a temporary loss of vision. Minimizing this loss of information is a prominent explanation for changes in blink rates and temporarily suppressed blinks, but quantifying this loss is difficult, as environmental regularities are usually complex and unknown. Here we used a controlled detection experiment with parametrically generated event statistics to investigate human blinking control. Subjects were able to learn environmental regularities and adapted their blinking behavior strategically to better detect future events. Crucially, our design enabled us to develop a computational model that allows quantifying the consequence of blinking in terms of task performance. The model formalizes ideas from active perception by describing blinking in terms of optimal control in trading off intrinsic costs for blink suppression with task-related costs for missing an event under perceptual uncertainty. Remarkably, this model not only is sufficient to reproduce key characteristics of the observed blinking behavior such as blink suppression and blink compensation but also predicts without further assumptions the well-known and diverse distributions of time intervals between blinks, for which an explanation has long been elusive.

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20

Sun, Wei, Xiaorui Zhang, Jian Wang, Jun He, and Srinivas Peeta. "Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/832621.

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An improved Bayesian fusion algorithm (BFA) is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA) method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA), radial basis function neural network (RBFNN), and Kalman filter (KF), are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability; also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.

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21

Kumari B.M, Kusuma, Sampada Sethi, Ramakanth Kumar P, Nishant Kumar, and Atulit Shankar. "Driver Drowsiness Detection System Using Sensors." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 3 (December 1, 2017): 139. http://dx.doi.org/10.11591/ijict.v6i3.pp139-145.

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<p>A low-cost and simple distributed sensors model that is particularly suitable for measuring eye blink of the driver, accident and hand position on a steering wheel. These sensors can be used in automotive active safety systems that aim at detecting driver’s fatigue, a major issue to prevent road accidents. The key point of this approach is to design a prototype of sensor units, so that it can serve as platform for integrating different kinds of sensors into the steering wheel. Since the sensors are attached to the steering wheel, therefore they can’t be detached by the driver. It will also detect dangerous stylish driving which may lead to fatal accidents. The major drawback is that the eye blink sensors frame worn by the driver can be removed causing the sensor non-operational. The outcome is that the vibrator attached to eye blink sensor’s frame vibrates if the driver shuts his eyes for approximately 3 seconds and also the LCD displays the respective warning message. The wheel is slowed or stopped depending on the condition. This is accompanied by the vehicle’s owner being notified through the GSM module, so the owner can retrieve the driver’s location, photograph and a list of nearby police stations through an android mobile application. Therefore, driver can be alerted during drowsiness and the owner can be notified simultaneously.</p>

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22

Poveda Zavala, Sebastián, Kelvin Ortíz Chicaiza, José Luis Murillo López, Johanna Cerezo Ramírez, and Sang Guun Yoo. "EEG Signal Processing Model for Eye Blink Detection." Journal of Engineering and Applied Sciences 15, no. 7 (March 14, 2020): 1671–75. http://dx.doi.org/10.36478/jeasci.2020.1671.1675.

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23

Abe, Kiyohiko, Shoichi Ohi, and Minoru Ohyama. "Automatic Blink Detection by the Frame Splitting Method." IEEJ Transactions on Electronics, Information and Systems 132, no. 9 (2012): 1437–45. http://dx.doi.org/10.1541/ieejeiss.132.1437.

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24

Toda, Takeshi, Kouhei Tsuruoka, Tatsuhiko Miyakawa, and Xinxin Liu. "Robust Blink Detection Method For Low Frame Rates." IEEJ Journal of Industry Applications 3, no. 5 (2014): 374–80. http://dx.doi.org/10.1541/ieejjia.3.374.

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25

Seki, M. "A study of blink detection using bright pupils." JSAE Review 19, no. 1 (January 1998): 58–61. http://dx.doi.org/10.1016/s0389-4304(97)00054-4.

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26

Hershman, Ronen, Avishai Henik, and Noga Cohen. "Novel Blink Detection Method Based on Pupillometry Noise." Journal of Vision 18, no. 10 (September 1, 2018): 590. http://dx.doi.org/10.1167/18.10.590.

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27

Chao, Lei, Wang Changyuan, Li Guang, and Shi Lu. "Detection of Blink State Based on Fatigued Driving." International Journal of Advanced Network, Monitoring and Controls 4, no. 4 (2019): 24–29. http://dx.doi.org/10.21307/ijanmc-2019-067.

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28

Fogelton, A., and W. Benesova. "Eye blink detection based on motion vectors analysis." Computer Vision and Image Understanding 148 (July 2016): 23–33. http://dx.doi.org/10.1016/j.cviu.2016.03.011.

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29

Królak, Aleksandra, and Paweł Strumiłło. "Eye-blink detection system for human–computer interaction." Universal Access in the Information Society 11, no. 4 (October 2, 2011): 409–19. http://dx.doi.org/10.1007/s10209-011-0256-6.

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30

Pantić, Mirjana, and Sunčica Zdravković. "VISUAL DETECTION OF STATIC OBJECTS AMONG DYNAMIC DISTRACTORS." Primenjena psihologija 9, no. 1 (April 7, 2016): 101. http://dx.doi.org/10.19090/pp.2016.1.101-118.

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Though a dynamic object, placed against stationary background, always grabs attention, opposite is not necessarily true. Hence, in this study we placed a stationary target among the dynamic distractors. We investigated whether visual detection depends on (1) set size (9, 18 or 27), (2) type of the distractor dynamics (jitter, blink, or luminance change) and (3) synchronisation (synchronized or unsynchronized distractors change). In contrast to pop-out effect of a dynamic target, the search for stationary target was serial, as the RT analysis revealed. The synchronisation of the distractor dynamic properties helped the detection especially in the larger sets. The most distracting for the target detection was illumination change of the distractors whereas the least distracting was blink.

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31

Shapiro, Kimron, Jon Driver, Robert Ward, and Robyn E. Sorensen. "Priming from the Attentional Blink: A Failure to Extract Visual Tokens but Not Visual Types." Psychological Science 8, no. 2 (March 1997): 95–100. http://dx.doi.org/10.1111/j.1467-9280.1997.tb00689.x.

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When people must detect several targets in a very rapid stream of successive visual events at the same location, detection of an initial target induces misses for subsequent targets within a brief period. This attentional blink may serve to prevent interruption of ongoing target processing by temporarily suppressing vision for subsequent stimuli. We examined the level at which the internal blink operates, specifically, whether it prevents early visual processing or prevents quite substantial processing from reaching awareness. Our data support the latter view. We observed priming from missed letter targets, benefiting detection of a subsequent target with the same identity but a different case. In a second study, we observed semantic priming from word targets that were missed during the blink. These results demonstrate that attentional gating within the blink operates only after substantial stimulus processing has already taken place. The results are discussed in terms of two forms of visual representation, namely, types and tokens.

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OHNISHI, Yuya, and Masashi KAWASUMI. "Non-Invasive Measurement in Detection Method of Eye Blink." Journal of Life Support Engineering 20, Supplement (2008): 173. http://dx.doi.org/10.5136/lifesupport.20.supplement_173.

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33

Saravanakumar, S., and N. Selvaraju. "Eye Tracking and Blink Detection for Human Computer Interface." International Journal of Computer Applications 2, no. 2 (May 10, 2010): 7–9. http://dx.doi.org/10.5120/634-873.

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Hershman, Ronen, Avishai Henik, and Noga Cohen. "A novel blink detection method based on pupillometry noise." Behavior Research Methods 50, no. 1 (January 16, 2018): 107–14. http://dx.doi.org/10.3758/s13428-017-1008-1.

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Jha, Aanchal, and M. Ganesh Lakshamana Kumar. "EMG extractor and blink detection for human health monitoring." International Journal of Engineering & Technology 7, no. 2 (May 10, 2018): 706. http://dx.doi.org/10.14419/ijet.v7i1.1.12648.

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In this paper we have proposed noise free EMG extractor for biomedical applications and also provide method for detecting blink signal. EMG signal is applied to preamplifier followed by Chebyshev filter and programmable gain amplifier further this processed EMG signal is applied to comparator to detect the blink. This topology is designed in UMC 180nm CMOS technology. Amplifier with gain of 81.155 dB and CMRR of 155.197 dB is designed. Preamplifier gain of 32.1244 dB with CMRR of 76.0743 dB which leads to common mode cancellation at priliminary stage. It also provide input referred noise ranges from 90 to 101.8636 µ V/sqrt(Hz) to reduce the noise for overall system. 4th order Chebyshev filter provides filtering with slope of -80 dB/decade with leads to reduce the unwanted signals. Filtered EMG signal is applied to programmable gain amplifier where gain ranges from 0 to 23 dB.It consumes power of 0.3µ W at 1V supply voltage.

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Kitazawa, Momoko, Michitaka Yoshimura, Kuo-Ching Liang, Satoshi Wada, Masaru Mimura, Kazuo Tsubota, and Taishiro Kishimoto. "Utilization of Facial Image Analysis Technology for Blink Detection." Eye & Contact Lens: Science & Clinical Practice 44 (November 2018): S297—S301. http://dx.doi.org/10.1097/icl.0000000000000513.

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Kalivaraprasad, VD M. Prasad, and L. Harshavardhan. "Development of Blink Restoration Model for Facial Paralysis Detection." Journal of Physics: Conference Series 1804, no. 1 (February 1, 2021): 012175. http://dx.doi.org/10.1088/1742-6596/1804/1/012175.

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38

Borza, Diana, Razvan Itu, and Radu Danescu. "In the Eye of the Deceiver: Analyzing Eye Movements as a Cue to Deception." Journal of Imaging 4, no. 10 (October 16, 2018): 120. http://dx.doi.org/10.3390/jimaging4100120.

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Deceit occurs in daily life and, even from an early age, children can successfully deceive their parents. Therefore, numerous book and psychological studies have been published to help people decipher the facial cues to deceit. In this study, we tackle the problem of deceit detection by analyzing eye movements: blinks, saccades and gaze direction. Recent psychological studies have shown that the non-visual saccadic eye movement rate is higher when people lie. We propose a fast and accurate framework for eye tracking and eye movement recognition and analysis. The proposed system tracks the position of the iris, as well as the eye corners (the outer shape of the eye). Next, in an offline analysis stage, the trajectory of these eye features is analyzed in order to recognize and measure various cues which can be used as an indicator of deception: the blink rate, the gaze direction and the saccadic eye movement rate. On the task of iris center localization, the method achieves within pupil localization in 91.47% of the cases. For blink localization, we obtained an accuracy of 99.3% on the difficult EyeBlink8 dataset. In addition, we proposed a novel metric, the normalized blink rate deviation to stop deceitful behavior based on blink rate. Using this metric and a simple decision stump, the deceitful answers from the Silesian Face database were recognized with an accuracy of 96.15%.

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Yuze, Hiroaki, and Hideoki Tada. "Automatic detection of eye blink wave and a tentative estimation of mental load by eye blink activities." Japanese journal of ergonomics 28, Supplement (1992): 344–45. http://dx.doi.org/10.5100/jje.28.supplement_344.

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40

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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KATO, Tsugumasa, and Hiroyuki MIYAMOTO. "2C2-1 Break reminder by blink detection in VDT work." Japanese Journal of Ergonomics 49, Supplement (2013): S322—S323. http://dx.doi.org/10.5100/jje.49.s322.

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42

Dou, Dou, and Zhaogong Zhang. "Blink Detection Based on Pixel Fluctuation Ratio of Eye Image." Journal of Physics: Conference Series 1453 (January 2020): 012073. http://dx.doi.org/10.1088/1742-6596/1453/1/012073.

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J.A, Ojo, Omilude L.T, and Adeyemo I.A. "Fatigue Detection in Drivers using Eye-Blink and Yawning Analysis." International Journal of Computer Trends and Technology 50, no. 2 (August 25, 2017): 87–90. http://dx.doi.org/10.14445/22312803/ijctt-v50p115.

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Lee, Jeong Su, Hong Ji Lee, Won Kyu Lee, Yong Gyu Lim, and Kwang Suk Park. "Development of Online Speller using Non-contact Blink Detection Glasses." Journal of Biomedical Engineering Research 36, no. 6 (December 31, 2015): 283–90. http://dx.doi.org/10.9718/jber.2015.36.6.283.

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Leonard, C. J., and H. Egeth. "Feature-based guidance improves singleton detection during the attentional blink." Journal of Vision 9, no. 8 (March 21, 2010): 152. http://dx.doi.org/10.1167/9.8.152.

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Wang, Mei, Lin Guo, and Wen-Yuan Chen. "Blink detection using Adaboost and contour circle for fatigue recognition." Computers & Electrical Engineering 58 (February 2017): 502–12. http://dx.doi.org/10.1016/j.compeleceng.2016.09.008.

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Chang, Won-Du, Ho-Seung Cha, Kiwoong Kim, and Chang-Hwan Im. "Detection of eye blink artifacts from single prefrontal channel electroencephalogram." Computer Methods and Programs in Biomedicine 124 (February 2016): 19–30. http://dx.doi.org/10.1016/j.cmpb.2015.10.011.

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Egambaram, Ashvaany, Nasreen Badruddin, Vijanth S. Asirvadam, Tahamina Begum, Eric Fauvet, and Christophe Stolz. "Online detection and removal of eye blink artifacts from electroencephalogram." Biomedical Signal Processing and Control 69 (August 2021): 102887. http://dx.doi.org/10.1016/j.bspc.2021.102887.

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P, Anantha Raman. "A Microcontroller based Car-Safety System: Implementing Drowsiness Detection and Eye Blink Detection in Parallel." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 1474–77. http://dx.doi.org/10.22214/ijraset.2019.3272.

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Sree Sharmila, T., R. Srinivasan, K. K. Nagarajan, and S. Athithya. "Eye Blink Detection Using Back Ground Subtraction and Gradient-Based Corner Detection for Preventing CVS." Procedia Computer Science 165 (2019): 781–89. http://dx.doi.org/10.1016/j.procs.2020.01.011.

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