• Journal of Engineering Education Research
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• v.25 no.2
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• pp.32-41
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• 2022

The purpose of this study was in a pandemic situation caused by COVID-19 to explore the online distance experiments and interaction of engineering freshmen, and to identify practical difficulties, resulting in to derive implications. Seven freshmen from engineering college participated in the interview, of which data were analyzed based on phenomenological analysis methods. The types of non-face-to-face experiments experienced by students were complete non-face-to-face experiment, mixed face-to-face experiment, and fusion face-to-face experiment. Students were completely isolated in time and space in complete non-face-to-face experiment. In biweekly mixed face-to-face experiment, isolation was halved. In fusion face-to-face experiment, isolation was removed. Non-face-to-face interactions can be characterized by restrictions on simultaneous activities, on rapport formation, and on observation opportunities. Based on these results, three implications were derived: First, it is necessary to allow students to manage time and space constraints on their own in non-face-to-face experiments. Second, support is needed to solve the difficulty of forming rapport, which is a characteristic of non-face-to-face interaction. Third, an opportunity to observe the interaction between other students and professors should be provided.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.179-186
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• 2022

In this paper, we design a heart rate measurement system using a web camera. In order to measure the heart rate, face image information is acquired and classified. The face image data is collected from web camera. The heart rate is measured using the collected face image data. We design a function to measure heart rate using input of face information using a web camera in non-contact manner. We design a function that reads face information and estimates heart rate by analyzing face color. An experiment was performed to compare the non-contact heart rate with the actual measured heart rate. The heart rate measurement system using a web camera proposed in this paper is a technology that can be used in various fields. It will be used in sports fields that require heart rate measurement at a low cost.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• Smart Media Journal
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• v.11 no.8
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• pp.65-72
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• 2022

Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.521-524
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• 2003

In this paper, we propose a face region extraction algorithm using color information and projection. After the extraction of face candidate image using adaptive color information, we project it into vertical direction to estimate the width of the face. Then the redundant parts of the face are efficiently removed by using the estimated face width. And the width information of the face is used at the horizontal projection step to extract the height of the face, and non-face region such as the neck and some background regions, which are represented as the similar skin color, effectively eliminated. From the experiment results for the various images, the proposed algorithm shows more accurate results than the conventional algorithm.

• Journal of Mechanical Science and Technology
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• v.14 no.3
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• pp.314-319
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• 2000

Back-face strain compliance (BFS compliance) for the four-point bend specimen has been calibrated for various crack length ratios. Finite element technique was employed to simulate four-point loading and calculate back-face strain of the bend specimen. The numerically determined strain variation along the back face indicates that the sensitivity to gage placement increases with crack length and back-face strain at the gage length less than O.2W, where W is the width of the bend specimen, can be measured within 5% deviation of the maximum BFS. Non-dimensional back-face strain compliance, -E'BCW, was calibrated with FE analysis and experiment. The experimentally determined compliance indicates good agreement with the numerical compliance and can be expressed as a function of crack length ratio.

• Journal of the Korean Society of Physical Medicine
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• v.15 no.1
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• pp.143-152
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• 2020

PURPOSE: This study examined the effects of videoconferencing-based telerehabilitation exercise program on the gait, knee function, and quality of life of patients with knee osteoarthritis. METHODS: Forty-eight subjects, who were diagnosed with osteoarthritis of the knee by the radiologic findings, history, and a physical examination, were assigned randomly to a Control group, Experiment group I, and Experiment group II. The control group did not perform any exercise program and were educated in understanding and managing the disease of knee osteoarthritis for only one hour. Experimental groups I and II were provided with an exercise guidelines book for knee osteoarthritis, and the same exercise programs were conducted by face-to-face visits and non-face-to-face using telerehabilitation for eight weeks, respectively. To verify the effectiveness of each exercise program, the gait speed, knee disability index, and health related quality of life were measured. All assessments were conducted twice before and after the intervention. RESULTS: The participants who underwent both face-to-face and telerehabilitation exercise programs showed an improved gait speed, knee function, and health-related quality of life. In particular, there was no significant difference between the telerehabilitation exercise group and the direct face-to-face exercise group in improving the knee joint function and health related quality of life. CONCLUSION: A these findings the telerehabilitation exercise program for patients with knee osteoarthritis can alternate or supplement the face-to-face exercise program. Therefore, the telerehabilitation exercise program should be used not only as a substitute supplement program but also as an intervention for various diseases.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.548-559
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• 2009

The cascade face detector learned by Adaboost algorithm, which was proposed by Viola and Jones, is state of the art face detector due to its great speed and accuracy. In spite of its great performance, it still suffers from false alarms, and more computation is required to reduce them. In this paper, we want to reduce false alarms with less computation using facial color. Using facial color information, proposed face detection model scans sub-window efficiently and adapts a fast face/non-face classifier at the first stage of cascade face detector. This makes face detection faster and reduces false alarms. For facial color filtering, we define a facial color membership function, and facial color filtering image is obtained using that. An integral image is calculated from facial color filtering image. Using this integral image, its density of subwindow could be obtained very fast. The proposed scanning method skips over sub-windows that do not contain possible faces based on this density. And the face/non-face classifier at the first stage of cascade detector rejects a non-face quickly. By experiment, we show that the proposed face detection model reduces false alarms and is faster than the original cascade face detector.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.847-850
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• 2000

This paper proposes an image compressing and template matching algorithm for face image using GHA (Generalized Hebbian Algorithm). GHA is a part of PCA (Principal Component Analysis), that has single-layer perceptrons and operates and self-organizing performance. We used this algorithm for feature extraction of face shape, and our simulations verify the high performance for the proposed method. The shape for face in the fact that the eigenvector of face image can be efficiently represented as a coefficient that can be acquired by a set of basis is to compress data of image. From the simulation results, the mean PSNR performance is 24.08[dB] at 0.047bpp, and reconstruction experiment shows that good reconstruction capacity for an image that not joins at leaning.