• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.137-145
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• 2014

This paper suggests an LDA-based long distance face recognition algorithm for intelligent surveillance system. The existing face recognition algorithm using single distance face image as training images caused a problem that face recognition rate is decreased with increasing distance. The face recognition algorithm using face images by actual distance as training images showed good performance. However, this also causes user inconvenience as it requires the user to move one to five meters in person to acquire face images for initial user registration. In this paper, proposed method is used for training images by using single distance face image to automatically create face images by various distances. The test result showed that the proposed face recognition technique generated better performance by average 16.3% in short distance and 18.0% in long distance than the technique using the existing single distance face image as training. When it was compared with the technique that used face images by distance as training, the performance fell 4.3% on average at a close distance and remained the same at a long distance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.281-289
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• 2022

Our society is rapidly changing with the core technology of the 4th industrial revolution, the emergence of a generation with new characteristics, and the untact era following the With Corona policy. Although the transition to untact is accelerating in the field of performing arts, in the case of opera, face-to-face performances are mainly conducted by experts and enthusiasts through on-site performances. Therefore, it is necessary to consider the uncertainty of creating opportunities through the influx of new customers such as the MZ generation and the departure of existing experts and enthusiasts. In this study, in order to examine these existing problems, we conducted literature review and case analysis, compared the opera face-to-face/non-face-to-face performance characteristics, derived an activation plan, and conducted expert interviews to secure the coherence and validity of the plan. In conclusion, we thought that it was difficult to improve the sound and sound quality, impairing the sense of presence and emotion due to many shortcomings when operating non-face-to-face as a music genre with the characteristics of opera. Therefore, we established the direction of activating the opera mainly face-to-face, but making good use of the advantages of non-face-to-face, which is not limited by region and time, and promoting the direction of activating face-to-face and non-face-to-face performances complementary to each other through the concept of cultural enjoyment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.176-191
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• 2021

Non-face-to-face lectures have become a necessity rather than an option since COVID-19, and in order to improve the quality of university education, it is necessary to explore the properties of non-face-to-face lectures and make active efforts to improve them. This study, focusing on this, aims to provide basic data necessary for decision-making for non-face-to-face lecture design by analyzing the relative importance and execution satisfaction of non-face-to-face lecture attributes for professors and students. Based on previous research, a questionnaire was constructed by deriving 4 factors from 1st layer and 17 from 2nd layer attributes of non-face-to-face lectures. A total of 180 valid samples were used for analysis, including 60 professors and 120 students. The importance of the non-face-to-face lecture properties was calculated by obtaining the weights for each stratified element through AHP(Analytic Hierachy Process) analysis, and performance satisfaction was calculated through statistical analysis based on the Likert 5-point scale. As a result of the AHP analysis, both the professor group and the student group had the same priority for the first tier factors, but there was a difference in the priorities between the second tier factors, so it seems necessary to discuss this. As a result of the IPA(Importance Performance Analysis) analysis, the professor group selected the level of interaction as an area to focus on, and it was confirmed that research and investment in teaching methods for smooth interaction are necessary. The student group was able to confirm that it is urgent to improve and invest in the current situation so that the system can be operated stably by selecting the system stability. This study uses AHP analysis for professors and students groups to derive relative importance and priority, and calculates the IPA matrix using IPA analysis to establish the basis for decision-making on future face-to-face and non-face-to-face lecture design and revision. It is meaningful that it was presented.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.3
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• pp.109-126
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• 2022

Due to the nature of small businesses, different corporate performance was found according to the leadership and marketing capabilities of managers. In particular, the presence or absence of corporate performance was confirmed through the manager's perception of the usability of non-face-to-face services. As a result of conducting a survey of executives of small businesses, it was found that the open leadership and marketing capabilities of small business managers had an effect on corporate performance and also had an effect on the convenience of non-face-to-face service use.

• Journal of Christian Education in Korea
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• v.69
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• pp.139-162
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• 2022

This study verified the effect of learning motivation on immersion by non-face-to-face class method. For this purpose, 101 college students majoring in early childhood education were selected as research subjects. The average age of the study subjects was 22.6 years old, and 51 students took non-real-time non-face-to-face classes, and 50 students took real-time non-face-to-face classes. The study measured the level of immersion and the type of learning motivation after the non-face-to-face class was finished. The measured data were analyzed using descriptive statistical analysis and multiple regression analysis. As a result, in the results for all students, the performance approach goal had the most influence on immersion, and the mastery goal orientation had the next effect. Performance avoidance orientation had no effect. For students in non-face-to-face classes, performance approach goal orientation had an effect on immersion, and for students in real-time non-face-to-face classes, mastery goal orientation had an effect. The implications that can be obtained from the results of this study are as follows. First, non-real-time non-face-to-face classes should cover basic knowledge and skills so that there are no mistakes and failures. Second, non-real-time non-face-to-face classes should allow tasks with appropriate difficulty to be performed with a deadline. Third, real-time non-face-to-face classes should lower the fear of mistakes and failures.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• 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 Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.305-309
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• 2006

In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory(ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.