• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.39-42
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• 2023

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Earthquakes and Structures
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• 제14권2호
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• pp.179-186
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• 2018

We present the accurate investigation the seismic behavior of the gravity retaining wall built near rock face based on numerical method. The retaining wall is a useful structure in geotechnical engineering, where the earthquake is a common phenomenon; therefore, the evaluation of the behavior of the retaining wall during an earthquake is essential. However, in all previous studies, the backfill behind the wall was usually approximated by a homogeneous region, while in contrast, in practice, in many cases retaining walls are used to support the soil pressure in, inhomogeneous, mountainous area. This suggests an accurate investigation of the problem, i.e., numerical analysis. The numerical results will be compared with some of recently proposed analytical methods to show the accuracy of the proposed method. We show that increasing the volume of the rock face yields decreasing the permanent horizontal displacement of the gravity retaining wall built near rock face. Besides, we see that the permanent horizontal displacement of the gravity retaining wall with homogenous backfill is more than permanent horizontal displacement of the gravity retaining wall case of the built near rock face in different frequency contents.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2004년도 정기총회 및 학술대회
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• pp.237-240
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• 2004

This paper describes a character retrieval system for TV programs and a set of novel algorithms for detecting and recognizing faces for the system. Our character retrieval system consists of two main components: Face Register and Face Recognizer. The Face Register detects faces in video frames and then guides users to register the detected faces of interest into the database. The Face Recognizer displays the appearance interval of each character on the timeline interface and the list of scenes with the names of characters that appear on each scene. These two components also provide a function to modify incorrect results. which is helpful to provide accurate character retrieval services. In the proposed face detection and recognition algorithms. we reduce the computation time without sacrificing the recognition accuracy by using the DCT/LDA method for face feature extraction. We also develop the character retrieval system in the form of plug-in. By plugging in our system to a cataloguing system. the metadata about the characters in a video can be automatically generated. Through this system, we can easily realize sophisticated on-demand video services which provide the search of scenes of a specific TV star.

• 한국산업정보학회논문지
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• 제23권2호
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• pp.1-9
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• 2018

본 논문은 조명변화에 민감하지 않고, 사진에 대한 오인식을 방지하기 위한 얼굴인식 방법을 제안한다. 제안한 방법은 적외선과 깊이 영상을 동시에 이용하며, 적외선 영상으로 조명변화의 민감성을 해결하고, 깊이 영상으로 사진과 같은 2차원 영상에 대한 오인식을 방지한다. 적외선과 깊이 영상을 동시에 이용한 얼굴 검출 방법과 얼굴 인식을 위한 특징 추출 및 매칭 방법을 구현하였으며, 모의실험을 통하여 기존 방법에 비해 얼굴인식의 정확도가 증가함을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1485-1490
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user, does face recognition and vital for many surveillance based systems. Advantage of face recognition when compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to decrease in dimension from of image acquisition step and various changes associated with face pose and background. Factors that deteriorate performance of face recognition are many such as distance from camera to face, lighting change, pose change, and change of facial expression. In this paper, we implement a new 3D active camera system to prevent various pose variation that influence face recognition performance and propose face recognition algorithm for intelligent surveillance system and mobile robot system.

• 한국추진공학회지
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• 제23권4호
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• pp.81-91
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• 2019

고체 로켓 모터 성능을 계산하기 위해 Face offsetting method를 사용하여 3차원 그레인 burn-back 해석을 수행하였다. 그레인 연소 형상 해석은 표면의 이동을 계산하는 이동 경계면 문제이다. 기존 연구에서는 다양한 이동 경계면 해석 기법이 그레인 burn-back 해석에 적용되었으나 결과가 불완전했다. 이에 본 연구에선 face offsetting method를 사용한 그레인 burn-back 해석 모듈을 개발하였다. Face offsetting method는 기존 해석 기법의 장점을 조합하여 강건하고 정밀한 이동 경계면 해석을 수행한다. 해석 결과, face offsetting method가 그레인 burn-back 해석에 유용함을 검증하였다.

• 한국정밀공학회지
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• 제14권3호
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• pp.82-88
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• 1997

In this paper, the development of a precision CNC lathe prototype for mirror surface machining is presented. To obtain high precision machining accuracy, a hydrostatically supported precision spindle and a sliding guideway with turcite pad are adopted as the motion elements. The machining accuracy of the prototype machine, and the motional accuracy of its motion elements are tested and evaluated to confirm the validity of the application of these elements on the prototype. The hydrostatic spindle shows 0.09 .mu. m of rotational accuracy and the guideway shows about 0.8 .mu. m/170mm of horizontal straightness. The sur- face roughness of cupper and aluminium cylinder machined by the prototype machine with diamond tool are 0.07 .mu. m and 0.10 .mu. m Rmax respectively. From these results, it is verified that the prototype lathe is avail- able for high precision machining.