• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.94-102
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• 2011

In this paper, a novel drowsiness detection algorithm using eye-blink pattern is proposed. The proposed drowsiness detection model using finite automata makes it easy to detect eye-blink, drowsiness and sleep by checking the number of input symbols standing for closed eye state only. Also it increases the accuracy by taking vertical projection histogram after locating the eye region using the feature of horizontal projection histogram, and minimizes the external effects such as eyebrows or black-framed glasses. Experimental results in eye-blinks detection using the JZU eye-blink database show that our approach achieves more than 93% precision and high performance.

• Journal of Korea Multimedia Society
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• v.20 no.6
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• pp.873-881
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• 2017

Prevention of drowsy driving is one of the important issues for safe driving. In this study, the algorithm for detection of drowsy driving has been developed. The algorithm was implemented by applying template matching and line profile, which detects eye blink. The accuracy of eye detection and blink detection was $97.45{\pm}3.67%$ and $98.50{\pm}0.92%$, which was resulted from the verification experiment that 21 subjects participated. Consequently, the algorithm is expected to be used to prevent sleep-deprived driving.

• Journal of Biomedical Engineering Research
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• v.36 no.6
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• pp.283-290
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• 2015

We proposed blink based online speller for the locked-in syndrome (LIS) patients, paralyzed in nearly all voluntary muscles expect for the eyes, with a simple and easy-to-use eye blink detection glasses. Electrooculogram (EOG) is the golden standard method of eye movement or blink measurement with Ag/AgCl electrodes. However, this method has several drawbacks such as skin irritation and dehydration of conductive gel. To resolve the shortcomings, we used a blink detection system based on a transparent capacitively coupled electrode, which is conductive indium tin oxide (ITO) films. The films make it possible to measure eye blink without direct skin contact and obstruction of field of view. We finally developed user-friendly blink based online speller with the blink detection system. To classify voluntary and non-voluntary blink, we used the double blink for command of the speller. The online speller experiment result with six healthy subjects shows that mean accuracy is 98.96% and letter per minute (LPM) is 4.73, which are better result by comparison with conventional P300 or auditory brain-computer interface (BCI) paradigm. The result of the experiment demonstrates the possibility of applying the proposed system as a communication method for the LIS patients.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.797-807
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• 2016

The discussion in this paper aims to introduce an approach to detect drowsiness with Android based smartphones using the OpenCV platform tools. OpenCV for Android actually provides powerful tools for real-time body's parts tracking. We discuss here about the maximization of the accuracy in real-time eye tracking. Then we try to develop an approach for detecting eye blink by analyzing the structure and color variations of human eyes. Finally, we introduce a time variable to capture drowsiness.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.331-334
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• 2000

This paper presents the method for the decision of eye states using the eye blink in video sequences. The entire procedure consists of two steps: in the first step, the accurate eye position is found in the input image by using symmetry information of faces and projection, and in the second step, the eye open/close state is decided by the horizontal and vertical projection. The method in this paper is also used for detecting drivers' fatigue in the drowsiness detection system.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.193-206
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• 2023

In recent years, and with the increased adoption of digital transformation and spending long hours in front of these devices, clinicians have observed that the prolonged use of visual display units (VDUs) can result in a certain symptom complex, which has been defined as computer vision syndrome (CVS). This syndrome has been affected by many causes, such as light refractive errors, poor computer design, workplace ergonomics, and a highly demanding visual task. This research focuses on eliminating one of CVSs, which is the eye dry syndrome caused by infrequent eye blink rate while using a smart device for a long time. This research attempt to find a limitation on the current tools. In addition, exploring the other use cases to utilize the solution based on each vertical and needs.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.56-59
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• 1996

The purpose of this study is to identify the cause of driver's drowsiness and to get information about driver's drowsiness from facial image using computer vision. We measured the driver's movements of a head and shoulders in the highway arid street. We also measured the eye blink duration and yawning duration of normal and drowsy drivers. from the results, we confirmed that the measurement of eye blink and yawning might be a way of drowsy detection.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.322-325
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• 1997

In this study, we propose a non-invasive method to detect the drowsiness of a driver. The computer vision technology was used to extract an eye, track eyelids and measure the parameters related to the blink. We examined the blink patterns of a driver in drowsy state. For the evaluation of our image processing algorithm, the blink patterns were compared with the measured EOG signals. The result showed that our algorithm might be available in detection of drowsiness.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.953-960
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• 2018

In recently, eye blink recognition, and face recognition are very popular and promising techniques. In some cases, people can use the photos and face masks to hack mobile security systems, so we propose an eye blinking detection, which finds eyes through the proportion of human face. The proposed method detects the movements of eyeball and the number of eye blinking to improve face recognition for screen unlock on the mobile devices. Experimental results show that our method is efficient and robust for the screen unlock on the mobile devices.