• The Journal of Asian Finance, Economics and Business
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• v.7 no.5
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• pp.205-216
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• 2020

This study seeks to empirically analyze the effects of core self-evaluation and adaptive selling behavior on sales performance for female salespersons engaged in door-to-door sales through the face-to-face channel in the wellness industry. This study seeks to examine the importance of adaptive selling, through, salespeople derive appropriate strategies in response to market changes. For female salespeople who use face-to-face channels, this study empirically investigated the relationship between core self-evaluation and adaptive selling, and effects on sales performance. A 31-item survey was constructed, based on prior research. We selected six door-to-door sales companies in South Korea and conducted one-to-one interviews with female salespeople in the Seoul metropolitan area and analyzed 208 pieces of significant data. Results demonstrated that among the core self-evaluation factors for female salespeople, self-esteem, self-efficacy, and neuroticism had an effect on adaptive selling factors, while locus of control did not. These factors were found to affect sales performance through the mediating role of adaptive selling. Improvements in the adaptive selling capabilities of female salespeople in charge of face-to-face channels positively affected sales performance. Management efforts are required to enhance self-esteem, self-efficacy, or neuroticism. These results suggest that companies should support enhancing individual adaptive selling capabilities of their salespeople.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• Journal of the Korean Society of School Health
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• v.35 no.1
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• pp.11-21
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• 2022

Purpose: This study investigated the relationship between satisfaction with fundamental nursing skills practicum, confidence in fundamental nursing skills performance and learning flow, and examined factors influencing satisfaction with practicum programs of fundamental nursing skills in face-to-face and online classes for nursing students amid COVID-19. Methods: The subjects of the study were 229 junior nursing students from two colleges of nursing located in D and C city, respectively. The collected data were analyzed with descriptive statistics, independent t-test, ANOVA, Kruskal-Wallis test, Pearson's correlation and hierarchical multiple regression, using SPSS/WINdows 23.0. Results: The subjects' satisfaction with practicum showed a high positive correlation with confidence in performance (r=.55, p<.001) and learning flow (r=.70, p<.001) in face-to-face classes, and their satisfaction with practicum showed a high positive correlation with confidence in performance (r=.56, p<.001) and learning flow (r=.73, p<.001) in online classes. The factors affecting the subjects' satisfaction with practicum were learning flow (β=.51, p<.001) and confidence in performance (β=.30, p<.001) for face-to-face classes, and motivation for application (β=.14, p=.034), learning flow (β=.58 p<.001) and confidence in performance (β=.19, p=.015) for online classes. These factors explained 53% and 60% of the satisfaction with practicum in face-to-face classes (F=23.07, p<.001) and online classes (F=20.66, p<.001), respectively. Conclusion: Learning flow and confidence in performance should be considered when developing learning strategy programs to improve nursing students' satisfaction with fundamental nursing skills practicum in both face-to-face and online classes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• Advanced Industrial SCIence
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• v.2 no.1
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• pp.39-44
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• 2023

The purpose of this study is to compare the performance and satisfaction of online classes and non-face-to-face mixed classes at universities during the COVID-19 pandemic. This study was conductedtargeted fourth-grade students taking adult nursing lectures at the Department of Nursing at one university. Class performance and class satisfaction were compared between students who participated in the non-face-to-face class and participated in the non-face-to-face mixed class. class performance, students' average scores out of 100 on the final exams were compared. Class satisfaction compared the average score of questionnaire on class satisfaction Class performance was high in online classes, Class satisfaction was higher in mixed classes than in non-face-to-face classes. In the future, it will be necessary to develop and operate various educational methods for university education in the post-COVID-19 era.

• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.8
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• pp.395-401
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• 2015

Recently, many research studies have been carried out on the efficiency of light-shelf daylighting systems, especially comparing performance improvements and the limitations of reflective surfaces and their lighting performance. In this study, a crystal face reflective surface is proposed. The objective of the study is to evaluate the lighting performance of a crystal face light-shelf through a performance study. The performance study was carried out in a full scale test-bed in order to calculate the light distribution and energy consumption utilizing the standard indoor illumination as an index. The conclusions of the performance study are as follows. 1) The optimal angle of incidence for daylighting for both the operable flat type light-shelf and the crystal face light-shelf are taken in the natural environment on the dates of the winter and summer solstices, as well as the autumn and spring equinoxes. 2) The application and installation of the crystal face light-shelf can produce a 29.9%~34.3% increase of light distribution within the indoor space. However, the increase of light distribution can also lead to a decrease in the uniformity ratio, a design challenge that should be considered when applying a crystal face light-shelf. 3) It is possible to achieve a 7.98%~13.3% greater reduction in energy consumption when applying a crystal face light-shelf than when applying a flat type light-shelf. The increase in the number of crystal faces should concur with the analysis of the energy reduction. A limitation of the study is that only one predetermined pattern was performance tested for a crystal face light-shelf. In order to carry out further research on crystal face light-shelves, additional performance studies are needed based on alternative patterns and designs.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.161-175
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• 2007

Face recognition technology has lately attracted considerable attention because of its non-intrusiveness, usability and applicability. Related companies insist that their commercial products show the recognition rates more than 95% according to their self-testing. But, the rates cannot be admitted as official recognition rates. So, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of face recognition systems. In this paper, I propose a reference model for biometrics recognition evaluation tools, and implement an evaluation tool for the face recognition system based on the proposed reference model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.201-210
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• 2023

Deep learning-based face verificattion model show high performance and are used in many fields, but there is a possibility the user's face image may be leaked in the process of inputting the face image to the model. Althoughde-identification technology exists as a method for minimizing the exposure of face features, there is a problemin that verification performance decreases when the existing technology is applied. In this paper, after combining the face features of other person, a de-identified face image is created through StyleGAN. In addition, we propose a method of optimizingthe combining ratio of features according to the face verification model using HopSkipJumpAttack. We visualize the images generated by the proposed method to check the de-identification performance, and evaluate the ability to maintain the performance of the face verification model through experiments. That is, face verification can be performed using the de-identified image generated through the proposed method, and leakage of face personal information can be prevented.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.15-22
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• 2019

There are many ways to perform face recognition. The first step of face recognition is the face detection step. If the face is not found in the first step, the face recognition fails. Face detection research has many difficulties because it can be varied according to face size change, left and right rotation and up and down rotation, side face and front face, facial expression, and light condition. In this study, facial features are extracted and the extracted features are geometrically reconstructed in order to improve face recognition rate in extracted face region. Also, it is aimed to adjust face angle using reconstructed facial feature vector, and to improve recognition rate for each face angle. In the recognition attempt using the result after the geometric reconstruction, both the up and down and the left and right facial angles have improved recognition performance.