• Journal of Engineering Education Research
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• v.25 no.1
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• pp.22-32
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• 2022

The purpose of this study is to design and operate non-face-to-face online practice class in engineering education, and to explore students' perceptions and effectiveness of them. To this end, after developing and implementing a strategy for non-face-to-face online practice activities in the 'Introduction to Electric Circuits and Lab' course, the questionnaire responses of 47 learners were analyzed, and the group differences were investigated using Kruskal-Wallis test. As a result, it was found that students' perceptions of non-face-to-face online practice class were positive in terms of learning effect, learning convenience, interaction, and satisfaction. The group difference according to the face-to-face/non-face-to-face preference method was found to be higher in the group that preferred non-face-to-face class in terms of learning convenience, interaction and satisfaction. As for the group difference according to the number of questions and answers of the learners, the group who answered the question showed a higher awareness of the learning convenience and interaction than the group that did not. In addition, implications for designing a learning environment for non-face-to-face online practice classes were presented.

• Korean Journal of Cleft Lip And Palate
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• v.16 no.1
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• pp.19-23
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• 2013

Maxilla protraction, orthopedic correction of deficient maxilla is necessary for treatment of most Class III growing patients. Protraction method has evolved from tooth-born appliance to miniplates on the maxilla, and from face mask to miniplates on the chin. By placing miniplates on the maxilla and the mandible and running elastics between them, we can minimize dentoalveolar relapse and maximize orthopedic change.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.11 no.1
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• pp.23-28
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• 2012

In this paper, we proposed and developed a fast and efficient real time face detection and recognition which can be run on embedded system instead of high performance desktop. In the face detection process, we detect a face by finding eye part which is one of the most salient facial features after applying various image processing methods, then in the face recognition, we finally recognize the face by comparing the current face with the prepared face database using a template matching algorithm. Also we optimized the algorithm in our system to be successfully used in the embedded system, and performed the face detection and recognition experiments on the embedded board to verify the performance. The developed method can be applied to automatic door, mobile computing environment and various robot.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.300-307
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• 2008

This paper is concerned with face recognition for human-robot interaction (HRI) in robot environments. For this purpose, we use Tensor Subspace Analysis (TSA) to recognize the user's face through robot camera when robot performs various services in home environments. Thus, the spatial correlation between the pixels in an image can be naturally characterized by TSA. Here we utilizes face database collected in u-robot test bed environments in ETRI. The presented method can be used as a core technique in conjunction with HRI that can naturally interact between human and robots in home robot applications. The experimental results on face database revealed that the presented method showed a good performance in comparison with the well-known methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in distant-varying environments.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1019-1026
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• 2015

In this paper, we propose a new method of eye, face, and lip makeup techniques on the target face image from several makeup examples without losing detail features such as eyelids, eyebrows, hair. After detection of the feature layer for the skin, we applied our makeup techniques to the target face by using a blending technique. We used a cartoon rendering using bilateral filter. In order to smoothly makeup the target face, we created two Gaussian Weight maps for natural skin makeup effects. Our method did not need to perform complex operations, so the makeup results are so natural. Our experimental results show good performances in various makeups.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.245-250
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• 2019

In this paper, we propose an ellipsoid modeling method for coding of a face depth picture. The ellipsoid modeling is firstly based on a point of a nose tip which is defined as the lowest value of the depth in the picture. The proposed ellipsoid representation is simplified through a difference of depth values between in the nose tip and in left or right boundary point of the face. Parameters of the ellipsoid are calculated through coordinates and depth values to minimize differences from the actual depth pixels. A picture is predicted by the modeled ellipsoid for coding of the face depth picture. In simulation results, an average MSEs between the face depth picture and the predicted picture is measured as 20.3.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.3-102
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• 2001

In this paper, we propose a novel method that can detect face region effectively with fuzzy theory and neural network We make fuzzy rules and membership functions to describe the face color. In this algorithm, we use a perceptually uniform color space to increase the accuracy and stableness of the nonlinear color information. We use this model to extract the face candidate, and then scan it with the pre-built sliding window by using a neural network-based pattern-matching method to find eye. A neural network examines small windows of face candidate, and decides whether each window contains eye. We can standardize the face candidate geometrically with detected eyes.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.

• Journal of Internet Computing and Services
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• v.5 no.1
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• pp.59-66
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• 2004

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. in this paper, it proposed symmetric by using symmetry region searching and Wavelet Transform to reduce computation time of symmetry region searching, and It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction region.

• 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.