• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.212-214
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• 2021

In a society with Covid-19 as part of our daily lives, we had to adapt ourselves to a new reality to maintain our lifestyles as normal as possible. An example of this is teleworking and online classes. However, several issues appeared on the go as we started the new way of living. One of them is the doubt of knowing if real people are in front of the camera or if someone is paying attention during a lecture. Therefore, we encountered this issue by creating a 3D reconstruction tool to identify human faces and expressions actively. We use a web camera, a lightweight 3D face model, and use the 2D facial landmark to fit expression coefficients to drive the 3D model. With this Model, it is possible to represent our faces with an Avatar and fully control its bones with rotation and translation parameters. Therefore, in order to reconstruct facial expressions during online meetings, we proposed the above methods as our solution to solve the main issue.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.217-220
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• 2007

In this paper, we present a new anchor shot detection system which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) anchor shot detection module using a support vector data description. According to our computer experiments, the proposed system shows not only the comparable accuracy to the recent other results, but also more faster detection rate than others.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.742-744
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• 2004

본 논문은 복잡한 배경에서의 얼굴 검출에 있어서 다중 특징 추출 데이터로 학습한 계단식 분류기에 의한 방법을 제안한다 얼굴 검출에서 얼굴의 패턴은 상당히 다양한 영상 표현으로 나타나기 때문에 하나의 특징 추출 방법은 사람의 얼굴을 모델링 하기에는 부족하다. 따라서 여기서는 얼굴의 전체적인 지역적인 특징을 나타내는 Subregion과, 얼굴의 주파수 특성에 따라 좀 더 세밀하고 다양한 속성들을 나타내는 Haar 웨이블릿 변환을 이용하여 다중으로 특징을 추출하여 효과적인 모델링을 시도하였다. 특징을 추출한 얼굴과 비얼굴의 패턴(pattern)을 구분하기 위해서 패턴들의 통계적인 특성을 이용하여 각 추출방법에 맞게 학습된 Bayesian 분류기를 직렬로 연결하여 사용하였으며 비얼굴은 얼굴과 유사한 비얼굴(face-like nonface) 패턴들을 사용하여 모델링 하였다. 제안한 얼굴 검출 방식의 성능은 MIT-CMU 시험 영상들을 이용하여 평가하였다. 그 결과 한 가지 특징 추출을 사용하는 것 보다 두 가지 특징 추출을 병행한 계단식 구성이 더 정확한 검출 결과를 나타내었다.

• Science of Emotion and Sensibility
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• v.4 no.2
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• pp.47-55
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• 2001

In affective space model for the faces of Korean women in twenties was developed based on the findings of Park, et. at. (2001) that suggested two orthogonal dimensions for the affective representation of a face. babyish-mature and sharp-soft. In the current study, affective facial characteristics were visualized by providing properly synthesized faces at 17 subregions of the model space Effect of physical attributes of a face on its affective evaluation was also investigated along the two affective dimensions. The relationship between typical adjectives describing facial affectiveness anti physical attributes of a face was examined to provide a category-based interpretation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.41-47
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• 2022

In this paper, we propose a new and simple self-supervised learning method that predicts the middle image of a face image sequence for automatic expression recognition. Automatic facial expression recognition can achieve high performance through deep learning methods, however, generally requires a expensive large data set. The size of the data set and the performance of the algorithm are tend to be proportional. The proposed method learns latent deep representation of a face through self-supervised learning using an existing dataset without constructing an additional dataset. Then it transfers the learned parameter to new facial expression reorganization model for improving the performance of automatic expression recognition. The proposed method showed high performance improvement for two datasets, CK+ and AFEW 8.0, and showed that the proposed method can achieve a great effect.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.519-526
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• 2022

Identifying and searching for characters appearing in scenes during multimedia video editing is an arduous and time-consuming process. Applying artificial intelligence to labor-intensive media editing tasks can greatly reduce media production time, improving the creative process efficiency. In this paper, a method is proposed which combines existing artificial intelligence based techniques to automate character recognition and search tasks for video editing. Object detection, face detection, and pose estimation are used for character localization and face recognition and color space analysis are used to extract unique representation information.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3211-3218
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• 1997

This paper addresses an eye location algorithm which is essential process of human face tracking system for natural video-conferencing. In current video-conferencing systems, user's facial movements are restricted by fixed camera, therefore it is inconvenient to users. We Propose an eye location algorithm for automatic face tracking. Because, locations of other facial features guessed from locations of eye and scale of face in the image can be calculated using inter-ocular distance. Most previous feature extraction methods for face recognition system are approached under assumption that approximative face region or location of each facial feature is known. The proposed algorithm in this paper uses no prior information on the given image. It is not sensitive to backgrounds and lighting conditions. The proposed algorithm uses the valley representation as major information to locate eyes. The experiments have been performed for 213 frames of 17 people and show very encouraging results.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• Archives of Plastic Surgery
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• v.44 no.3
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• pp.223-227
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• 2017

Background Carpal tunnel release is one of the most common surgical procedures performed by hand surgeons. The authors created a surgical simulation of open carpal tunnel release utilizing a mobile and rehearsal platform app. This study was performed in order to validate the simulator as an effective training platform for carpal tunnel release. Methods The simulator was evaluated using a number of metrics: construct validity (the ability to identify variability in skill levels), face validity (the perceived ability of the simulator to teach the intended material), content validity (that the simulator was an accurate representation of the intended operation), and acceptability validity (willingness of the desired user group to adopt this method of training). Novices and experts were recruited. Each group was tested, and all participants were assigned an objective score, which served as construct validation. A Likert-scale questionnaire was administered to gauge face, content, and acceptability validity. Results Twenty novices and 10 experts were recruited for this study. The objective performance scores from the expert group were significantly higher than those of the novice group, with surgeons scoring a median of 74% and medical students scoring a median of 45%. The questionnaire responses indicated face, content, and acceptability validation. Conclusions This mobile-based surgical simulation platform provides step-by-step instruction for a variety of surgical procedures. The findings of this study help to demonstrate its utility as a learning tool, as we confirmed construct, face, content, and acceptability validity for carpal tunnel release. This easy-to-use educational tool may help bring surgical education to a new- and highly mobile-level.