• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2112-2115
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• 2003

The system for the real time face detection is described in this paper. For face verification, support vector machine (SVM) was utilized. Although SVM performs quit well, SVM has a drawback that the computational cost is high because all pixels in a mask are used as an input feature vector of SVM. To resolve this drawback, a method to reduce the dimension of feature vectors using the integer DCT was proposed. Also for the real time face detection applications, low-complexity methods for face candidate detection in a gray image were used. As a result, the accurate face detection was performed in real time.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• Journal of Engineering Education Research
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• v.25 no.6
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• pp.93-102
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• 2022

Due to COVID-19, all activities in society are emphasized non-face-to-face, and the educational environment is changing without exception. Looking at the results of the survey after conducting non-face-to-face education, there was a lot of rejection of non-face-to-face practical education. The biggest reason was that instructors were not familiar with the non-face-to-face education method, and feedback was not smooth during or after education. In particular, software practice education was not easy to share the software development environment, but communication and feedback on class contents and tasks were important. In particular, if face-to-face and non-face-to-face are alternately variable, it is not easy for practical education to be consistently connected. Even if non-face-to-face hands-on education is changed to face-to-face hands-on education, we will present a plan to use a data sharing system such as question-and-answer, assignment, practice content, and board content so that it can proceed smoothly. This study presents an efficient software education process that can provide learners with a software integrated practice environment based on a shared server, question-and-answer between instructors and learners, and share feedback on tasks. For the verification of the presented process, the effectiveness was confirmed through the survey results by applying the face-to-face/non-face-to-face education process to 220 trainees for 30 months in software education classes such as A university hands-on education, B company new employees, and ICT education courses.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09b
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• pp.200-211
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• 2010

Tunnel Mapper, which is tunnel face survey system was used to conduct Face Mapping on the face of the tunnel that is under construction. Then, accuracy and utility value on the forecast of discontinuity were verified to verify the field application in order to present the measures for the use of the system for conducting research on the discontinuity. As result of the directivity verification following discontinuity‘s project, forecasted measurement and actually researched measurement error for the Dip direction and Dip angle was less than ${\pm}10$. Accuracy was 82.6% for Dip direction and 90.7% for Dip angle, which are high. Accordingly, face research discontinuity forecasting system's reliability level towards directivity is high. Tunnel Mapper, a tunnel face survey system can be leveraged to replace face's visual survey and to obtain objective information, enabling execution of the survey system that can automate face survey going beyond time and space related limitations.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.13-20
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• 2004

In the real-time automatic face recognition technique, accurate face detection is essential and very important part because it has the effect to face recognition performance. In this paper, we use color information, edge information, and binary information to detect candidate regions of eyes from Input image, and then detect face candidate region using the center point of the detected eyes. We verify both eye candidate region and face candidate region using Support Vector Machines(SVM). It is possible to perform fast and reliable face detection because we can protect false detection through these verification process. From the experimental results, we confirmed the Proposed algorithm in this paper shows excellent face detection rate over 99%.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.244-252
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• 2015

This paper describes the design and implementation of a System-on-a-Chip (SoC) for face recognition to use in wearable/mobile products. The design flow starts from the system specification to implementation process on silicon. The entire process is carried out using a FPGA-based prototyping platform environment for design and verification of the target SoC. To ensure that the implemented face recognition SoC satisfies the required performances metrics, time analysis and recognition tests were performed. The motivation behind the work is a single chip implementation of face recognition system for target applications.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.11-17
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• 2010

In this parer, face recognition method which can be applied to ITCS (interactive TV control system) is proposed. We extracted ULBP(uniform local binary pattern) histogram feature from infra-red images, and we detected left-right eyes and face region by using SVM classifier. Then, We implemented face recognition system which is using Gabor transform and ULBP histogram feature and applied to personal verification for ITCS.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.867-868
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• 2008

In this paper, we discuss face verification using the optimum nonlinear composite filter. The optimum nonlinear composite filter is derived by minimizing the output energy due to the additive noise and the input scene. We construct the nonlinear composite filter with 3 training images. The performance is analyzed in terms of the detection probability and the false alarm probability showing the robustness to distortion and noise.