• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.804-813
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• 2021

The purpose of this study was on the learning satisfaction of dental hygiene students according to the face-to-face and non-face-to-face teaching methods in the COVID-19 educational environment. A self-reported questionnaire was completed by 122 dental hygiene students of G University located in Gwangju from October to November, 2020. The general characteristics, instructional characteristics, teaching methods, and learning satisfaction were investigated, and the collected data were analyzed using SPSS 18.0. The effective practical teaching method chosen by the students was face-to-face, and there was a significant difference according to the class choice. The learning satisfaction according to the general characteristics showed a significant difference in the preferred practice method for improving instrument technique. The face-to-face classes showed significantly higher learning satisfaction in terms of checking on doing well study during class and the convenience. In addition, as a result of analyzing the factors influencing learning satisfaction, the choice of face-to-face class was confirmed as a significant variable. Therefore, in order to increase the learning satisfaction of students, it is necessary to design a class that essentially includes face-to-face class when planning a practice class.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.570-576
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• 2005

Recognition of posed face is one of the most challenging problems in the field of face recognition. In this paper, as a preprocessing step for recognizing such faces, a method to transform non-frontal face images into frontal face images is proposed. The linear relationship between eigenfaces is utilized to obtain a pose transform matrix. The proposed method is verified with a well-known face recognition algorithm based on PCA/LDA. Compared to the conventional algorithm applied to the original posed face images, our experimental results indicated that the proposed method contributes to improve the recognition rate of such faces by $20\%$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3144-3164
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• 2019

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal ($0^{\circ}$) face sample is available in gallery and seven poses ($0^{\circ}$, ${\pm}30^{\circ}$, ${\pm}35^{\circ}$, and ${\pm}45^{\circ}$) as a probe on the LFW and the CMU Multi-PIE databases.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1252-1255
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• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.123-129
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• 2021

This Study is to identify factors that affect the interaction between professors and university students and their satisfaction with non-face-to-face lectures. The subjects were 348 university student who attended from October 5 to October 23, 2020. The statistics program was SPSS win 22.o. Among the expected benefits of non-face-to-face classes, the temporal benefit was 3.69 points, the expected benefit of the learning effect was 3.46 points, and the technical familiarity was 3.47 points. Satisfaction with non-face-to-face classes was found to be 3.58 points. Factors affecting the satisfaction of lectures in non-face-to-face classes were expected benefits of learning effect, university satisfaction, technical familiarity, expected benefits over time, and the number of non-face-to-face classes desired for the next semester. Learning effect The higher the expected benefit, the higher the university satisfaction, the higher the technical familiarity, the higher the expected temporal benefit, the higher the number of non-face-to-face classes desired for the next semester, the higher the satisfaction with the non-face-to-face class lectures. Therefore, it was confirmed that the role of the instructor was important in the interaction between the instructor and university students in the non-face-to-face class and the satisfaction of the lecture.

• The Journal of Korean Medicine
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• v.42 no.1
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• pp.1-11
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• 2021

Objectives: This pilot study aimed to evaluate the agreement between traditional face-to-face Korean medicine (KM) pattern identification and non-face-to-face KM pattern identification using the data from related questionnaires, tongue image, and pulse features in patients with cancer. Methods: From January to June 2020, 16 participants with a cancer diagnosis were recruited at the one Korean medicine hospital. Three experienced Korean medicine doctors independently diagnosed the participants whether they belong to the cold pattern or not, heat pattern or not, deficiency pattern or not, and excess pattern or not. Another researcher collected KM pattern related data using questionnaires including Cold-Heat Pattern Identification (CHPI), tongue image analysis system, and pulse analyzer. Collected KM pattern related data without participants' identifier was provided for the three Korean medicine doctors in random order, and non-face-to-face KM pattern identification was carried out. The kappa value between face-to-face and non-face-to-face pattern identification was calculated. Results: From the face-to-face pattern identification, there were 13/3 cold/non-cold pattern, 4/12 heat/non-heat pattern, 14/2 deficiency/non-deficiency pattern, and 0/16 excess/non-excess pattern participants. In cold/non-cold pattern, kappa value was 0.455 (sensitivity: 0.85, specificity: 0.67, accuracy: 0.81). In heat/non-heat pattern, the kappa value was 0.429 (sensitivity: 0.75, specificity: 0.72, accuracy: 0.75). The kappa value of deficiency/non-deficiency and excess/non-excess pattern was not calculated because of the few participants of non-deficiency, and excess pattern. Conclusions: The agreement between traditional face-to-face pattern identification and non-face-to-face pattern identification seems to be moderate. The non-face-to-face pattern identification using questionnaires, tongue, and pulse features may feasible for the large clinical study.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.49-52
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• 2002

This paper presents a fast algorithm for face detection from color images on internet. We use Mahalanobis distance between standard skin color and actual pixel color on IQ color space to segment skin color regions. The skin color regions are the candidate face region. Further, the locations of eyes and mouth regions are found by computing average pixel values on horizontal and vertical pixel lines. The geometry of mouth and eye locations is compared to the standard face geometry to eliminate false face regions. Our Method is simple and fast so that it can be applied to face search engine for internet.

• Science of Emotion and Sensibility
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• v.4 no.1
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• pp.7-14
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• 2001

Based on the past research works on the relationship between face type and personality, personality and purchasing behavior, personality and preference for color, face type and preference for color, we assumed that there could be certain differences in preferred color tone in purchasing automobile according to face type. Objective of this paper is to analyze what differences there are preferred color tones of purchasing automobile by face type. The questionnaires on preferred color tone of automobile were investigated, and the tone of color were classified into light, dark, brilliant, plain tones, and the differences of preferred color tone of purchasing automobile were analyzed by the face types. The result showed the facts that compared with the other types, the rectangular face type preferred the light tone of color, whereas the other face types little showed a distinctive inclination for a particular color tone. Results of this research could be utilized for automobile sales policy for materials of research into color tones, provided some problems are fixed and the concrete researches into relationship between face type and personality, purchasing behavior, preference for color are carried out.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.