• Journal of the Ergonomics Society of Korea
• /
• v.30 no.1
• /
• pp.195-202
• /
• 2011

Objective: The aim of this study is to investigate and review the previous researches about objective measuring fatigue caused by visual stimuli. Also, we analyze possibility of alternative visual fatigue measurement methods using facial expression recognition and gesture recognition. Background: In most previous researches, visual fatigue is commonly measured by survey or interview based subjective method. However, the subjective evaluation methods can be affected by individual feeling's variation or other kinds of stimuli. To solve these problems, signal and image processing based visual fatigue measurement methods have been widely researched. Method: To analyze the signal and image processing based methods, we categorized previous works into three groups such as bio-signal, brainwave, and eye image based methods. Also, the possibility of adopting facial expression or gesture recognition to measure visual fatigue is analyzed. Results: Bio-signal and brainwave based methods have problems because they can be degraded by not only visual stimuli but also the other kinds of external stimuli caused by other sense organs. In eye image based methods, using only single feature such as blink frequency or pupil size also has problem because the single feature can be easily degraded by other kinds of emotions. Conclusion: Multi-modal measurement method is required by fusing several features which are extracted from the bio-signal and image. Also, alternative method using facial expression or gesture recognition can be considered. Application: The objective visual fatigue measurement method can be applied into the fields of quantitative and comparative measurement of visual fatigue of next generation display devices in terms of human factor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.200-203
• /
• 2021

This paper introduces a smart mirror technology that recognizes a person's facial expressions through image classification among several artificial intelligence technologies and presents them in a mirror. 5 types of facial expression images are trained through artificial intelligence. When someone looks at the smart mirror, the mirror recognizes my expression and shows the recognized result in the mirror. The dataset fer2013 provided by kaggle used the faces of several people to be separated by facial expressions. For image classification, the network structure is trained using convolution neural network (CNN). The face is recognized and presented on the screen in the smart mirror with the embedded board such as Raspberry Pi4.

• Journal of Internet Computing and Services
• /
• v.7 no.3
• /
• pp.143-153
• /
• 2006

Facial feature detection is a basic technology in applications such as human computer interface, face recognition, face tracking and image database management. The speed of feature detection algorithm is one of the main issues for facial feature detection in real-time environment. Primary factors like a variation by lighting effect, location, rotation and complex background give an effect to decrease a detection ratio. A facial feature detection algorithm is proposed to improve the detection ratio and the detection speed. The proposed algorithm detects skin regions over the entire image improved by CLAHE, an algorithm for light compensation against varying lighting conditions. To extract facial feature points on detected skin regions, it uses appearance-based geometrical characteristics of a face. Since the method shows fast detection speed as well as efficient face-detection ratio, it can be applied in real-time application to face tracking and face recognition.

• International Journal of Internet, Broadcasting and Communication
• /
• v.8 no.4
• /
• pp.39-47
• /
• 2016

This study is a smart tourism research based on face recognition applied system that manages individual information of foreign tourists to smartphone. It is a way to authenticate by using face recognition, which is biometric information, as a technology applied to identification inquiry, immigration control, etc. and it is designed so that tourism companies can provide customized service to customers by applying algorism to smartphone. The smart tourism system based on face recognition is a system that prepares the reception service by sending the information to smartphone of tourist service company guide in real time after taking faces of foreign tourists who enter Korea for the first time with glasses attached to the camera. The smart tourism based on face recognition is personal information recognition technology, speech recognition technology, sensing technology, artificial intelligence personal information recognition technology, etc. Especially, artificial intelligence personal information recognition technology is a system that enables the tourism service company to implement the self-promotion function to commemorate the visit of foreign tourists and that enables tourists to participate in events and experience them directly. Since the application of smart tourism based on face recognition can utilize unique facial data and image features, it can be beneficially utilized for service companies that require accurate user authentication and service companies that prioritize security. However, in terms of sharing information by government organizations and private companies, preemptive measures such as the introduction of security systems should be taken.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.8
• /
• pp.1136-1141
• /
• 2022

Researches of face recognition on masked faces have been increasingly important due to the COVID-19 pandemic. To realize a stable and practical recognition performance, large amount of facial image data should be acquired for the purpose of training. However, it is difficult for the researchers to obtain masked face images for each human subject. This paper proposes a novel method to synthesize a face image and a virtual mask pattern. In this method, a pair of masked face image and unmasked face image, that are from a single human subject, is fed into a convolutional autoencoder as training data. This allows learning the geometric relationship between face and mask. In the inference step, for a unseen face image, the learned convolutional autoencoder generates a synthetic face image with a mask pattern. The proposed method is able to rapidly generate realistic masked face images. Also, it could be practical when compared to methods which rely on facial feature point detection.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.917-924
• /
• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.425-426
• /
• 2021

Recently, there have been frequent incidents of talent accidents caused by youth driving without a license. Driving without a license is becoming a hotbed of curiosity and challenge for some young people, and there is a limit to managing smart keys at home to prevent this. Therefore, in this paper, using the facial recognition algorithm, the face of the driver sitting in the driver's seat is compared with the information stored in advance, and the system is designed to control the engine by determining that it is a registered driver. If the registered driver authentication is successful, the matching accuracy and Unlock message are output to the LCD connected to the Raspberry Pi.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.754-774
• /
• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• Journal of the Korea Safety Management & Science
• /
• v.24 no.1
• /
• pp.31-38
• /
• 2022

The most common symptoms of COVID-19 are high fever, cough, headache, and fever. These symptoms may vary from person to person, but checking for "fever" is the government's most basic measure. To confirm this, many facilities use thermographic cameras. Since the previously developed thermographic camera measures body temperature one by one, it takes a lot of time to measure body temperature in places where many people enter and exit, such as multi-use facilities. In order to prevent malfunctions and errors and to prevent sensitive personal information collection, this research team attempted to develop a facial recognition thermographic camera. The purpose of this study is to compensate for the shortcomings of existing thermographic cameras with disaster safety IoT integrated solution products and to provide quarantine systems using advanced facial recognition technologies. In addition, the captured image information should be protected as personal sensitive information, and a recent leak to China occurred. In order to prevent another case of personal information leakage, it is urgent to develop a thermographic camera that reflects this part. The thermal imaging camera system based on facial recognition technology developed in this study received two patents and one application as of January 2022. In the COVID-19 infectious disease disaster, 'quarantine' is an essential element that must be done at the preventive stage. Therefore, we hope that this development will be useful in the quarantine management field.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.47 no.1
• /
• pp.7-16
• /
• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.