• 한국콘텐츠학회논문지
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• 제21권6호
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• pp.804-813
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• 2021

본 연구는 코로나-19로 인한 일부 치위생과 신입생의 대면과 비대면 수업방법에 따른 학습만족도에 대한 연구로 신입생의 학습만족도를 높이기 위한 방안을 마련하고자 시행하였다. 2020년 10월부터 11월까지 광주광역시에 소재한 G대학 학생 122명을 대상으로 설문조사를 실시했다. 대상자의 일반적인 특성, 수업적 특성, 수업방법, 학습만족도를 조사하였고 수집된 자료는 SPSS 18.0을 사용하여 분석했다. 학생들이 선택한 효율적인 실기수업방법은 대면수업이었고 수업선택에 따라 유의한 차이가 있었다. 일반적인 특성에 따른 학습만족도는 술기능력향상을 위해 선호하는 실습수업방법에서 유의한 차이를 나타냈다. 대면수업과 비대면수업의 전체학습만족도는 유의한 차이를 보이지 않았지만 수업 중의 점검과 선택한 수업방법의 편리성 항목에서 대면수업이 유의하게 높은 학습만족도를 나타냈다. 또한 학습만족도에 영향을 미치는 요인을 분석한 결과 대면수업선택이 유의한 변수로 확인되었다. 따라서 신입생의 학습만족도를 높이기 위해 실습수업 계획 시 대면수업이 필수적으로 포함된 수업을 설계하는 것이 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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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.

• 한국통신학회논문지
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• 제30권6C호
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• pp.570-576
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• 2005

얼굴 인식 분야에서 포즈의 변화는 인식률을 저하시키는 가장 심각한 문제로 알려져 있다. 본 논문에서는 이러한 포즈가 변화된 얼굴 영상에 대한 인식률을 높이기 위한 전처리 단계로 정면이 아닌 얼굴 영상을 정면 얼굴 영상으로 변환시키는 방법을 제안한다. 제안한 방법은 PCA 계수를 선형 변환 시키는 변환 행렬을 사용되는데 이 변환 행렬은 PCA 계수 사이의 선형적인 관계를 이용하여 구한다. 제안된 방법은 PCA/LDA를 이용한 얼굴 인식 알고리즘으로 검증하였으며, 실험 결과 제안된 방법이 얼굴 인식률을 $20\%$ 정도 향상시킴을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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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.

• 산업융합연구
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• 제19권6호
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• pp.123-129
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• 2021

교수자와 대학생의 학습효과 및 비대면 강의만족도에 영향을 주는 요인을 파악하고자 한다. 2020년 10월5일부터 10월 23일까지 J지역과 K지역의 대학생을 대상으로 348부 시행하였다. 통계프로그램은 SPSS win 22.o을 이용하였다. 비대면수업 기대효익 중 시간적 기대효익은 3.69점, 학습효과 기대효익은 3.46점으로 나타났고, 기술적 친숙도는 3.47점으로 나타났다. 비대면수업 강의만족도는 3.58점으로 나타났다. 비대면수업 강의만족도에 영향을 미치는 요인은 학습효과 기대효익, 대학 만족도, 기술적 친숙도, 시간적 기대효익, 다음 학기 희망하는 비대면 수업 과목 수이었다. 학습효과 기대효익이 높을수록, 대학 만족도가 높을수록, 기술적 친숙도가 높을수록, 시간적 기대효익이 높을수록, 다음 학기 희망하는 비대면수업 과목 수가 많을수록 비대면수업 강의만족도가 높은 것으로 나타났다. 따라서 비대면수업의 교수자와 대학생의 학습효과 및 강의만족도에 있어서 교수자의 역할이 중요하다 것을 확인하였다.

• 대한한의학회지
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• 제42권1호
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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.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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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.

• 감성과학
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• 제4권1호
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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.

• 한국멀티미디어학회논문지
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• 제7권12호
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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.