• The Journal of Industrial Distribution & Business
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• v.12 no.11
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• pp.17-31
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• 2021

Purpose: Despite the fact that face (i.e. Chemyon) is deeply-rooted in Korean culture and significantly affects the behavior of Korean people, the effect of face on leisure consumers' consumption behavior has only reported mixed findings, that is, significant and/or insignificant face effects have been reported. It is necessary to integrate prior research findings, and comprehensively examine the effect of face on leisure consumers' consumption behavior. The purpose of this study was to investigate the effect (i.e. effect size, and moderating variables) of face on leisure consumers' consumption behavior through meta-analysis. Research design, data and methodology: Among 1,019 face-related academic studies, retrieved from the academic research information services (RISS), 34 studies and 300 cases examining the effect of face on leisure consumers' consumption behavior were finally included for meta-analysis. Face measured as face sensitivity and/or a face sensitivity sub-component (shame-consciousness, formality-consciousness, and other-consciousness) were integrated in the meta-analysis. Leisure consumers' consumption behavior was classified as antecedents of purchase (overall conspicuous consumption tendency, overall symbolic consumption tendency, personality, high price, high quality, brand seeking, fashion seeking, enjoyment, other person (interpersonal) consideration, position, reference group, and attitude), purchase (purchase intention, unplanned purchase, purchase, and expenditure), and post-purchase (satisfaction, repurchase, and post-purchase). The data used in the meta-analysis was comprised of correlation coefficients, and the meta-analysis was performed using the R-program. Results: The overall mean effect size of face on leisure consumers' consumption behavior was .248. It was found that the effect size was the largest in the order of shame-consciousness face, formality-consciousness face, and other-consciousness face. Among the types of leisure consumers' consumption behavior categorized as dependent variables, the effect size was found to be largest in the order of position, attitude, reference group, post-purchase behavior, brand seeking, personality, trend seeking, etc. In addition, it was found that the leisure types moderated the effect size of face on leisure consumers' consumption behavior. The effect size was found to be largest in the order of skin diving, baseball, various leisure participation, dance, gambling, golf, etc. Conclusions: Face moderately or significantly influence leisure consumers' consumption behavior.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.777-786
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• 2008

Human face avatar is important information in nowadays, such as describing real people in virtual world. In this paper, we have presented a face avatar creation and warping algorithm by using face feature analysis method, in order to detect face feature, we utilized local mean method based on facial feature appearance and face geometric information. Then detect facial candidates by using it's character in $YC_bC_r$ color space. Meanwhile, we also defined the rules which are based on face geometric information to limit searching range. For analyzing face feature, we used face feature points to describe their feature, and analyzed geometry relationship of these feature points to create the face avatar. Then we have carried out simulation on PC and embed mobile device such as PDA and mobile phone to evaluate efficiency of the proposed algorithm. From the simulation results, we can confirm that our proposed algorithm will have an outstanding performance and it's execution speed can also be acceptable.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.91-98
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• 2019

Many researchers have attempted to recognize three-dimensional faces using feature points extracted from two-dimensional facial photographs. However, due to the limit of flat photographs, it is very difficult to recognize faces rotated more than 15 degrees from original feature points extracted from the photographs. As such, it is difficult to create an algorithm to recognize faces in multiple angles. In this paper, it is proposed a new algorithm to recognize three-dimensional face recognition based on feature points extracted from a flat photograph. This method divides into six feature point vector zones on the face. Then, the vector value is compressed and expanded according to the rotation angle of the face to recognize the feature points of the face in a three-dimensional form. For this purpose, the average of the compressibility and the expansion rate of the face data of 100 persons by angle and face zone were obtained, and the face angle was estimated by calculating the distance between the middle of the forehead and the tail of the eye. As a result, very improved recognition performance was obtained at 30 degrees of rotated face angle.

• 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.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.1-27
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• 2022

As the COVID-19 virus became prevalent worldwide, non-face-to-face contact was implemented in various ways, and the education system also began to draw much attention due to rapid non-face-to-face contact. The purpose of this study is to analyze the direction of non-face-to-face education in line with the continuously changing educational environment to date. In this study, data were visualized using Textom and Ucinet6 analysis tool programs to collect social network big data with various opinions. As a result of the study, keywords related to "COVID-19" were dominant, and keywords with high frequency such as "article" and "news" existed. As a result of the analysis, various issues related to non-face-to-face education, such as network failures and security issues, were identified. After the analysis, the direction of the non-face-to-face education system was studied according to the growth of the education market and changes in the educational environment. In addition, there is a need to strengthen security and feedback on teaching methods in non-face-to-face education analyzed using big data.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.613-621
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• 2022

The purpose of this study is to understand the transition experience from face-to-face lectures by nursing professors to non-face-to-face lectures. In this study, 17 nursing professors who have conducted non-face-to-face lectures for at least two semesters were collected through in-depth individual interviews according to the empirical phenomenological research method and analyzed according to the qualitative topic analysis method. The research results described the transition experiences of non-face-to-face lectures, focusing on the topics of change and development process according to non-face-to-face lectures, relationship with students, self-relationship, social context and sociality. In conclusion, nursing professors are expected to receive sufficient support to autonomously select non-face-to-face lectures using advanced technologies according to the trend of social change.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.35-41
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• 2017

We propose a face recognition and notification system that can transform visual face information into tactile signals in order to help visually impaired people. The proposed system consists of a glasses type camera, a mobile computer and an electronic cane. The glasses type camera captures the frontal view of the user, and sends this image to mobile computer. The mobile computer starts to search for human's face in the image when obstacles are detected by ultrasonic sensors. In a case that human's face is detected, the mobile computer identifies detected face. At this time, Adaboost and compressive sensing are used as a detector and a classifier, respectively. After the identification procedures of the detected face, the identified face information is sent to controller attached to a cane using a Bluetooth communication. The controller generates motor control signals using Pulse Width Modulation (PWM) according to the recognized face labels. The vibration motor generates vibration patterns to inform the visually impaired person of the face recognition result. The experimental results of face recognition and notification system show that proposed system is helpful for visually impaired people by providing person identification results in front of him/her.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.45-50
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• 2021

In order for SW education to be established in the era of non-face-to-face caused by COVID-19, research on the efficiency of SW education according to face-to-face and non-face classes is needed. Therefore, this study classified the operation status of the curriculum of 30 SW gifted classes nationwide in 2020 according to the class method(face-to-face, non-face, and blended). Subsequently, the results of class time and production per person were compared and analyzed through quantitative analysis. According to the study, the type of classes that performed the most classes compared to the planned number of hours was non-face-to-face(90.9%), followed by face-to-face(84.2%) and the least was blended(80.5%). The average number of products per student was the highest in the face-to-face class(0.504), while the blended class(0.421) and non-face-to-face class(0.42). Based on the results of this study, the non-face-to-face approach is advantageous in securing the number of hours, but various measures should be prepared to solve this problem because teachers and students find it difficult to guide the output.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05a
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• pp.106-109
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.187-194
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• 2021

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author's own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.