• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.15-25
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• 2017

Recently, technologies are being developed to recognize and authenticate users using bioinformatics to solve information security issues. Biometric information includes face, fingerprint, iris, voice, and vein. Among them, face recognition technology occupies a large part. Face recognition technology is applied in various fields. For example, it can be used for identity verification, such as a personal identification card, passport, credit card, security system, and personnel data. In addition, it can be used for security, including crime suspect search, unsafe zone monitoring, vehicle tracking crime.In this thesis, we conducted a study to recognize faces by detecting the areas of the face through a computer webcam. The purpose of this study was to contribute to the improvement in the accuracy of Recognition of Face Based on CNN Algorithms. For this purpose, We used data files provided by github to build a face recognition model. We also created data using CNN algorithms, which are widely used for image recognition. Various photos were learned by CNN algorithm. The study found that the accuracy of face recognition based on CNN algorithms was 77%. Based on the results of the study, We carried out recognition of the face according to the distance. Research findings may be useful if face recognition is required in a variety of situations. Research based on this study is also expected to improve the accuracy of face recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.835-838
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• 2003

Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.5
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• pp.369-378
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• 2010

This paper presents a new approach of closing the tunnel face with sprayed concrete to reduce the stress at the tunnel face and displacement occurring at the ground surface during tunnel excavation. In order to carry out this research, the experimental and numerical studies are performed. In the experimental study, the model tests are carried out according to the closure ratio of tunnel face, tunnel depth and tunnel excavation length. The model test results are analyzed and interpreted by numerical calculation in order to verify both results obtained from experimental and numerical studies. It is clearly found that the tunnel face stability is decreased in decreasing the closure ratio of tunnel face. The results also show that the tunnel face is stable when the closure ratio of tunnel face is larger than 80%. This research will be very useful to develop the economical tunnel face closing system.

• Asia pacific journal of information systems
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• v.9 no.3
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• pp.1-23
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• 1999

The objective of this study is to examine the media richness theory and the social information processing model by analyzing the effect of media experience on media perception and communication process. To accomplish this objective, a laboratory experiment was conducted. The independent variable was text medium experience and a face-to-face medium was added as a control group. The dependent variables were medium perception and communication process. Medium perception includes perceived richness, medium feeling, task satisfaction, and communication satisfaction. Communication processes were also analyzed to compare each treatment group. The results can be summarized into two facts. First, face-to-face group showed higher perceived richness than text medium group. And experienced text medium group perceived their text medium richer than inexperienced text medium group. Second, experienced text medium groups showed more interactions between subjects than inexperienced text medium group. Experienced text medium group also showed more agreements and meta-communication which could be found in face-to-face group. The result of this study supported media richness theory by finding that face-to-face medium was perceived richer than text medium, And the results also proved social information processing model by comparing experienced text medium group and inexperienced text medium group. The text medium, although thought to be the leanest one, could be perceived richer if users had lots of experience on it.

• Smart Media Journal
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• v.7 no.4
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4080-4097
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• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• Journal of the Korean Geotechnical Society
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• v.28 no.4
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• pp.79-89
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• 2012

This paper presents experimental and numerical studies on the closure ratio of tunnel face to reduce pressure and displacement occurring at tunnel face during tunnel excavation. In experimental study, model tests are carried out according to the closure ratio of tunnel face and tunnel depth. Model test results are analyzed and interpreted by numerical calculation in order to verify results obtained from experimental and numerical studies. It is clearly found that tunnel face stability increases with the increase of the closure ratio of tunnel face. The results also show that tunnel face is stable when the closure ratio of tunnel face is larger than 80%. This research will be very useful to develop the economical tunnel face closing system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.50-56
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• 2020

With the rapid development of deep neural network theory and application research, the effect of face detection has been improved. However, due to the complexity of deep neural network calculation and the high complexity of the detection environment, how to detect face quickly and accurately becomes the main problem. This paper is based on the relatively simple model of the MTCNN model, using FDDB (Face Detection Dataset and Benchmark Homepage), LFW (Field Label Face) and FaceScrub public datasets as training samples. At the same time of sorting out and introducing MTCNN(Multi-Task Cascaded Convolutional Neural Network) model, it explores how to improve training speed and Increase performance at the same time. In this paper, the dynamic image pyramid technology is used to replace the traditional image pyramid technology to segment samples, and OHEM (the online hard example mine) function in MTCNN model is deleted in training, so as to improve the training speed.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.283-293
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• 2019

Evaluating the stability of the excavation face of the cross-river shield tunnel with good accuracy is considered as a nonlinear and multivariable complex issue. Understanding the stability evaluation method of the shield tunnel excavation face is vital to operate and control the shield machine during shield tunneling. Considering the instability mechanism of the excavation face of the cross-river shield and the characteristics of this engineering, seven evaluation indexes of the stability of the excavation face were selected, i.e., the over-span ratio, buried depth of the tunnel, groundwater condition, soil permeability, internal friction angle, soil cohesion and advancing speed. The weight of each evaluation index was obtained by using the analytic hierarchy process and the entropy weight method. The evaluation model of the cross-river shield construction excavation face stability is established based on the idea point method. The feasibility of the evaluation model was verified by the engineering application in a cross-river shield tunnel project in China. Results obtained via the evaluation model are in good agreement with the actual construction situation. The proposed evaluation method is demonstrated as a promising and innovative method for the stability evaluation and safety construction of the cross-river shield tunnel engineerings.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.127-133
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• 2021

In this study, an augmented reality-based make-up tool was built to analyze the user's face shape based on face-type reference model data and to provide virtual makeup by providing face-type makeup. To analyze the face shape, first recognize the face from the image captured by the camera, then extract the features of the face contour area and use them as analysis properties. Next, the feature points of the extracted face contour area are normalized to compare with the contour area characteristics of each face reference model data. Face shape is predicted and analyzed using the distance difference between the feature points of the normalized contour area and the feature points of the each face-type reference model data. In augmented reality-based virtual makeup, in the image input from the camera, the face is recognized in real time to extract the features of each area of the face. Through the face-type analysis process, you can check the results of virtual makeup by providing makeup that matches the analyzed face shape. Through the proposed system, We expect cosmetics consumers to check the makeup design that suits them and have a convenient and impact on their decision to purchase cosmetics. It will also help you create an attractive self-image by applying facial makeup to your virtual self.