• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.510-512
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• 2023

In order to enhance a model's capability for detecting facial expressions, this research suggests a pipeline that makes use of the GradCAM component. The patching module and the pseudo-labeling module make up the pipeline. The patching component takes the original face image and divides it into four equal parts. These parts are then each input into a 2Dconvolutional layer to produce a feature vector. Each picture segment is assigned a weight token using GradCAM in the pseudo-labeling module, and this token is then merged with the feature vector using principal component analysis. A convolutional neural network based on transfer learning technique is then utilized to extract the deep features. This technique applied on a public dataset MMI and achieved a validation accuracy of 96.06% which is showing the effectiveness of our method.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.1-7
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• 2013

This study was aimed to evaluate the characteristics of impact sound of golf driver according to impact points of its face. In order to get the consistent impact sounds, the apparatus for free golf ball drop was prepared and used. Timed amplitude patterns and maximum spectral peaks of the impact sounds were variant according to the impact points of driver face. As an alternative method of impact sound analysis, cumulative sum of spectral power (cumsum) was used to distinguish between impact sounds according to the impact positions. From the comparison of frequencies representing 20%, 40%, 60%, 80% of cumsum of impact sound, 40% cumsum frequency of the center of driver face was lower than that of the toe and the heel. This finding suggests that the impact sound from the center of driver face has higher spectral power of low frequency component than that of the toe and heel.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.19-26
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• 2010

This paper proposes a classifier based on rectangular feature to detect face in real time. The goal is to realize a strong detection algorithm which satisfies both efficiency in calculation and detection performance. The proposed algorithm consists of the following three stages: Feature creation, classifier study and real time facial domain detection. Feature creation organizes a feature set with the proposed five rectangular features and calculates the feature values efficiently by using SAT (Summed-Area Tables). Classifier learning creates classifiers hierarchically by using the AdaBoost algorithm. In addition, it gets excellent detection performance by applying important face patterns repeatedly at the next level. Real time facial domain detection finds facial domains rapidly and efficiently through the classifier based on the rectangular feature that was created. Also, the recognition rate was improved by using the domain which detected a face domain as the input image and by using PCA and KNN algorithms and a Class to Class rather than the existing Point to Point technique.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.55-62
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• 2005

This paper proposes a method to recognize a face using DCT/LDA where LDA is applied to DCT coefficients of an input face image. In the proposed method, SSS problem of LDA due to less number of training data than the size of feature space can be avoided by expressing an input image in low dimensional space using DCT coefficients. In terms of the recognition rate, both the proposed method and the PCA/LDA method have shown almost equal performance while the training time of the proposed method is much shorter than the other. This is because DCT has the fixed number of basis vectors while the property of energy compaction rate is similar to that of PCA. Although depending on the number of coefficients employed for the recognition, the experimental results show that the performance of the proposed method in terms of recognition rate is very comparable to PCA/LDA method and other DCT/LDA methods, and it can be trained 13,000 times faster than PCA/LDA method.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.227-232
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• 2010

This paper proposes the real time lip reading method in the embedded environment. The embedded environment has the limited sources to use compared to existing PC environment, so it is hard to drive the lip reading system with existing PC environment in the embedded environment in real time. To solve the problem, this paper suggests detection methods of lip region, feature extraction of lips, and awareness methods of phonetic words suitable to the embedded environment. First, it detects the face region by using face color information to find out the accurate lip region and then detects the exact lip region by finding the position of both eyes from the detected face region and using the geometric relations. To detect strong features of lighting variables by the changing surroundings, histogram matching, lip folding, and RASTA filter were applied, and the properties extracted by using the principal component analysis(PCA) were used for recognition. The result of the test has shown the processing speed between 1.15 and 2.35 sec. according to vocalizations in the embedded environment of CPU 806Mhz, RAM 128MB specifications and obtained 77% of recognition as 139 among 180 words were recognized.

• Annals of Clinical Microbiology
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• v.21 no.4
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• pp.69-74
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• 2018

Laboratory medicine is a specialized division that supports physicians in the care of patients by providing rapid and accurate in vitro diagnostic tests. Standardization of every component of a specific test is essential for producing accurate results. The Clinical and Laboratory Standards Institute (CLSI) was founded to develop a formal consensus process for standardization in 1968, and has been publishing standards and guidelines covering all aspects of clinical, research, and other laboratory work. CLSI guidelines are widely used around the world for standardization. The CLSI antimicrobial susceptibility testing subcommittee (AST SC) consists of 6 standing and many ad hoc working groups. Members of the AST SC review submitted proposals and suggestions, decide on approving these submissions in face-to-face meetings held twice a year, and revise CLSI documents accordingly. As these face-to-face meetings are open to anyone who registers to attend, I strongly encourage the members of our Society to attend and actively participate in document development.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.95-111
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• 2021

Recently, many organizations are actively adopting VDI (Virtual Desktop Infrastructure), an IT-based business system, to build a non-face-to-face business environment for smart-work. However, most of the existing research on VDI has focused on the satisfaction of system service quality or the use of IT resources and investment for VDI introduction. However, research on effective management and utilization of factors according to the characteristics of VDI technology is urgently required. This study is an empirical research study on how VDI technology characteristics affect interactions and work performance by identifying differences in utilization factors between general organization members and IT managers, presenting standards for business utilization and management. This study proposed a model and hypothesis that the system technology characteristics for VDI use are mediated by interactions in which users respond to functions appropriate to their work. In order to verify the hypothesis, a questionnaire survey was conducted on 188 people of companies and institutions that have adopted and used VDI through a questionnaire survey. Data analysis was performed with partial least squares (PLS), a structural equation modeling (SEM) technique that uses a component-based approach to estimation. As a result of the empirical analysis, the same environmental function for performing work, N-th security, and remote access function factors for non-face-to-face work have a significant effect on interactivity, and IT managers have an additional significant effect on the management technology characteristics of resource reallocation. Has been shown to affect. The results of this study aim to minimize trial and error due to new introduction by presenting considerations for future VDI introduction through case analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.4
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• pp.82-92
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• 2006

In the field of image recognition, research on face recognition has recently attracted a lot of attention. The most important step in face recognition is automatic eye detection researched as a prerequisite stage. Existing eye detection methods for focusing on the frontal face can be mainly classified into two categories: active infrared(IR)-based approaches and image-based approaches. This paper proposes an eye region detection method in non-frontal faces. The proposed method is based on the edge--based method that shows the fastest computation time. To extract eye region in non-frontal faces, the method uses edge orientationhistogram of the global region of faces. The problem caused by some noise and unfavorable ambient light is solved by using proportion of width and height for local information and relationship between components for global information in approximately extracted region. In experimental results, the proposed method improved precision rates, as solving 3 problems caused by edge information and achieves a detection accuracy of 83.5% and a computational time of 0.5sec per face image using 300 face images provided by The Weizmann Institute of Science.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Korean Journal of Cognitive Science
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• v.13 no.3
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• pp.1-10
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• 2002

This study was performed to analyze the physical attributes of the faces and affective words on the fares. For analyzing physical attributes inside of a face, 36 facial features were selected and almost of them were the lengths or distance values. For analyzing facial contour 14 points were selected and the lengths from nose-end to them were measured. The values of these features except ratio values normalized by facial vortical length or facial horizontal length because the face size of each person is different. The principal component analysis (PCA) was performed and four major factors were extracted: 'facial contour' component, 'vortical length of eye' component, 'facial width' component, 'eyebrow region' component. We supposed the five-dimensional imaginary space of faces using factor scores of PCA, and selected representative faces evenly in this space. On the other hand, the affective words on faces were collected from magazines and through surveys. The factor analysis and multidimensional scaling method were performed and two orthogonal dimensions for the affections on faces were suggested: babyish-mature and sharp-soft.