• Journal of Korean Academy of Nursing
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• v.20 no.3
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• pp.381-398
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• 1990

Nursing involves deep human interpersonal relationships between nurses and patients. But in modem Korea, the nurse - patient relationship tends to be ritualistic and mechanestic. Patients usually express the hope that nurses be more tender and kind. Patients expect nurses to express their warmth especially through nonverbal behaviour. This study was conducted to identify patients' preferences for nurse's nonverbal expressions of warmth. Through the confirmation of these preferences, nurses may learn how to enhance their interpersonal relationships with patients. Subjects for the study were 73 patients who had been admitted to a university teaching hospital for at least three days and agreed to be interviewed by the investigator. The interactions were studied nonverbal expressions of warmth during nursing rounds and administration of oral medication. The interview schedule was expecially designed by the investigator to measure the nurse's posture, the distance between the nurse and the patient, the nurse's eye contact, facial expression, hand motion and head nodding. Data analysis included frequencies, percentages and X²-test. The results of this study may be summerized as follows : 1. Patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds. Preferred nurse's posture was sitting(50.7%) or standing(49.3%) opposite the patient. Preferred distance between the nurse and the patient was close to the bed(93.2%), less than 1m. Preferred eye contact was directed to the patient's eyes or their affected part (41.1%). Preferred facial expression was a smile(97.3%). Preferred hand motions were light gestures(41.1%). Patients preferred head nodding which approved their own opinions(69.9%). 2. Patient's preferences for nurse's nonverval expressions of warmth during administration of oral medication. Preferred nurse's posture was standing and waiting to confirm that the medication had been taken(58.9%). Preferred distance from the patient was at arm's length, 0.5-1m(64.4%). Patients preferred direct eye contact(58.9%) and a smile(94.5%). Patients preferred that the nurse put the medicine directly the patient's hand(64.4%). Whether the nurse nodded her head or not was not considered important. 3. The relation of general characteristics and patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds and administration of oral medication. During nursing rounds, the age of subjects(p＝0.010) and the standard of education(p＝0.026) related to the distance between the nurse and the patient. The sick hospital ward related to the eye contact(p＝0.017) and facial expression(p＝0.010). During administration of oral medication, the age of subjects(p＝0.044) and days of hospital treatment (p＝0.043) and the sick hospital ward(p＝0.0004) related to the facial expression. From this study, nurses can learn what kind nonverbal expressions of warmth are preferred by patients during rounds and administration and thus will enhance nurse- patient interpersonal relationships.

• Korean Journal of Cognitive Science
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• v.29 no.1
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• pp.39-59
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• 2018

To elucidate the effect of working memory (WM) load on negative facial emotion processing, we examined ERP components (P1 and N170) elicited by fearful and neutral expressions each of which was presented during 0-back (low-WM load) or 2-back (high-WM load) tasks. During N-back tasks, visual objects were presented one by one as targets and each of facial expressions was presented as a passively observed stimulus during intervals between targets. Behavioral results showed more accurate and fast responses at low-WM load condition compared to high-WM load condition. Analysis of mean amplitudes of P1 on the occipital region showed significant WM load effect (high-WM load > low-WM load) but showed nonsignificant facial emotion effect. Analysis of mean amplitudes of N170 on the posterior occipito-temporal region showed significant overall facial emotion effect (fearful > neutral), but, in detail, significant facial emotion effect was observed only at low-WM load condition on the left hemisphere, but was observed at high-WM load condition as well as low-WM load condition on the right hemisphere. To summarize, facial emotion effect observed by N170 amplitudes was modulated by WM load only on the left hemisphere. These results show that early emotional processing of negative facial expression could be eliminated or reduced by high load of WM on the left hemisphere, but could not be eliminated by high load on the right hemisphere, and suggest right hemispheric lateralization of negative facial emotion processing.

• Fashion & Textile Research Journal
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• v.14 no.5
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• pp.803-810
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• 2012

This study investigates the effect of nonverbal communication on trust, switching barrier, and repurchase intention. Sample subjects used in this study were customers of a fashion shop in Sunchon. The questionnaires were conveniently sampled from July 2010 to August 2010. Questionnaire data from 335 customers of a national brand were analyzed through a reliability analysis, factor analysis, and multiple regression analysis. The results of this study are as follows. First, nonverbal communication by the service provider was divided into 3 types, physical appearance and paralanguage, postures and proxemics, and facial expressions. Second, it was found that physical appearance and paralanguage, postures and proxemics, facial expression of nonverbal communication had a significant impact on customer trust. Third, given the relationship between nonverbal communication and switching barrier, it was represented that the postures and proxemics and facial expressions (except physical appearance and paralanguage) had a significantly positive influence on the switching barrier. Forth, physical appearance/paralanguage, postures/proxemics, and facial expressions (nonverbal communication) had a positive influence on repurchase intention. Fifth, given the relationship between trust and repurchase intention as well as switching barrier and repurchase intention, it was represented that trust and switching barrier have a significantly positive influence upon repurchase intention. According to the results of this study, the more positive nonverbal communication by the service provider then the higher the customer repurchase intention as well as trust and switching barrier. Fifth, given the relationship between trust and repurchase intention as well as switching barrier and repurchase intention, it was represented that trust and switching barrier have a significantly positive influence upon repurchase intentions.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.85-92
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• 2020

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.299-308
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• 2009

AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.179-186
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• 2017

Because facial asymmetry is caused by various causes, the cause analysis is important and quantitative index is needed to the evaluation. In this study, we applied the Nottingham Grading System that was used as a quantitative index to evaluate the facial paralysis by tracking the markers through the image processing and calculating the distance between the markers with images obtained by using the webcam, to evaluate facial asymmetry. The existing Nottingham Grading System has a problem of causing a measurement error in the specific case because the left and right are compared by summing the distance change between the feature points of the face part according to the change of the facial expression. We compared the case of the facial asymmetry and case of normal subject by using the existing Nottingham Grading System and the improved Nottingham grading system. In the existing Nottingham Grading System, case of facial asymmetry and case of facial symmetry were 99.0% and 95.0% respectively in the normal range, but the improved Nottingham Grading System showed facial asymmetry case was 74.0% and facial symmetrical case was 93.2%. The results of experiment show that the improved Nottingham Grading System allows detailed evaluation of each site and improved the problem of the Nottingham Grading System for specific cases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.295-298
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• 2007

본 논문에서는 사람의 얼굴표정을 통해 4개의 기본감정(기쁨, 슬픔, 화남, 놀람)에 대한 특징을 추출하고 인식하여 그 결과를 이용하여 감정표현 시스템을 구현한다. 먼저 주성분 분석(Principal Component Analysis)법을 이용하여 고차원의 영상 특징 데이터를 저차원 특징 데이터로 변환한 후 이를 선형 판별 분석(Linear Discriminant Analysis)법에 적용시켜 좀 더 효율적인 특징벡터를 추출한 다음 감정을 인식하고, 인식된 결과를 얼굴 표현 시스템에 적용시켜 감정을 표현한다.

• Korean Journal of Veterinary Research
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• v.44 no.3
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• pp.335-341
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• 2004

This study was undertaken to examine the developmental expression of osteopontin(OPN) in the mouse brain. In Western blotting analysis, the expression of OPN was noted initially at embryonic stage and increased gradually after birth and decreased at postnatal day 60(P60). In immunohistochemistry, OPN expression was found in the interstitial nucleus Cajal and the substantia nigra reticularis in anterior part of the brain and in the inferior olivary complex, the parabrachial nucleus, the facial nucleus, the gigantocellular reticular nucleus, the trigeminal nucleus and the anterior interposed nucleus in posterior part of the brain at P31 and P60. In addition, OPN expression in widespread neurons appeared during the period of neuronal differentiation, increased just after birth and decreased with maturation. These results suggest that OPN contributes to developmental processes, including the differentiation and maturation of specific neuronal populations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.