• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2547-2554
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• 2009

In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1129-1135
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• 2007

Human face detection technique plays an important role in computer vision area. It has lots of applications such as face recognition, video surveillance, human computer interface, face image database management, and querying image databases. In this paper, a fast face detection approach using Principal Component Analysis (PCA) and Support Vector Machines (SVM) is proposed based on the previous study on face detection technique. In the proposed detection system, firstly it filter the face potential area using statistical feature which is generated by analyzing the local histogram distribution the detection process is speeded up by eliminating most of the non-face area in this step. In the next step, PCA feature vectors are generated, and then detect whether there are faces present in the test image using SVM classifier. Finally, store the detection results and output the results on the test image. The test images in this paper are from CMU face database. The face and non-face samples are selected from the MIT data set. The experimental results indicate the proposed method has good performance for face detection.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.167-174
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• 2013

TIn this paper, we find the face in color images and the ability to track the face was implemented. Face tracking is the work to find face regions in the image using the functions of the computer system and this function is a necessary for the robot. But such as extracting skin color in the image face tracking can not be performed. Because face in image varies according to the condition such as light conditions, facial expressions condition. In this paper, we use the skin color pixel extraction function added lighting compensation function and the entire processing system was implemented, include performing finding the features of eyes, nose, mouth are confirmed as face. Lighting compensation function is a adjusted sine function and although the result is not suitable for human vision, the function showed about 4% improvement. Face features are detected by amplifying, reducing the value and make a comparison between the represented image. The eye and nose position, lips are detected. Face tracking efficiency was good.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.27 no.1
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• pp.1-11
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• 2021

Background: The purpose of this study is to identify the elements for the construction of the Korean academy of orthopedic manipulative physical therapy's (KAOMPT's) non-face-to-face lecture system using the Delphi method. Methods: The Delphi method was applied to 50 expert panel members of the Central Committee and the Provincial Branch of the KAOMPT. The Delphi survey was conducted in two rounds, and the first Delphi survey collected opinions on 40 questions on 12 topics. The second Delphi survey was collected into 25 questions on 4 topics. As a result of the survey, the content validity ratio (CVR), consensus and convergence were measured. Referring to the number of expert panels and previous studies were determine a CVR of at least 2.29, a consensus of at least .75 and a convergence of 0 to .5. Result: In the first Delphi result, out of the total 40 items, 20 items with high content validity ratio were found, and 10 items found double agreement. In the second Delphi result, 13 out of the total 25 items had a content validity ratio higher than 2.29, and 5 items found a double agreement. Conclusion: This study derived items on the role of central and municipal councils, lecture support and lecture room construction, non-face regular course and special lecture operation and personnel for the establishment of non-face-to-face lecture system. Based on this content, it is expected that it will help establish a non-face-to-face lecture system in 2021 through a pilot non-face-to-face lecture that will be implemented in the future.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.25-33
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• 2006

1. Objectives Recently there have been studied the trials to take out the characteristics of Sasangin's face. 3 dimensional modeling is essential to find out sasangin's face. So the studies of standard face model and face type are necessary. 2. Methods I have reviewed the researches of standard facial modeling and facial type in the inside and outside of the country. 3. Results and Conclusions The Facial Definition Parameters are a very complex set of parameters defined by MPEG-4. It has defineds set of 84 feature points and 68 Facial Animation Parameters. Face type has been researched to divide into male and female, or the westerns and the orientals, or sasangin(Taeyangin, Taeumin, Soyangin, Soeumin).

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1268-1280
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• 2015

In video surveillance system, the exposure of a person's face is a serious threat to personal privacy. To protect the personal privacy in large amount of videos, an automatic face detection method is required to locate and mask the person's face. However, in real-world surveillance videos, the effectiveness of existing face detection methods could deteriorate due to large variations in facial appearance (e.g., facial pose, illumination etc.) or degraded face (e.g., occluded face, low-resolution face etc.). This paper proposes a new face detection method based on multi-level facial features. In a video frame, different kinds of spatial features are independently extracted, and analyzed, which could complement each other in the aforementioned challenges. Temporal domain analysis is also exploited to consolidate the proposed method. Experimental results show that, compared to competing methods, the proposed method is able to achieve very high recall rates while maintaining acceptable precision rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.259-269
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• 2011

The B$\'{e}$k$\'{e}$sy audiometer is used to determine not only hearing threshold but also assumption of recruit phenomenon and cause of disease. However, current B$\'{e}$k$\'{e}$sy audiometer takes an operational model, so called face-to-face model, in which model one audiometrist can assess only one subject at a time. As a result, the face-to-face model leads to high audiometrist's labor cost and long waiting time when there exist many subjects. To improve such weakness, this paper extends PC based B$\'{e}$k$\'{e}$sy audiometer to support client/server model in which model one audiometrist enables to concurrently access several subjects. As providing capabilities such as real-time transfer of assessment result, video monitoring of subject and video chat, the same effect as face-to-face model but overcomes weakness of face-to-face model.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.353-362
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• 2005

Recently, study of Bimodal recognition has become very active. In this paper we propose a Bimodal user recognition system that uses face information and audio information. Face recognition consists of face detection step and face recognition step. Face detection uses AdaBoost to find face candidate area. After finding face candidates, PCA feature extraction is applied to decrease the dimension of feature vector. And then, SVM classifiers are used to detect and recognize face. Audio recognition uses MFCC for audio feature extraction and HMM is used for audio recognition. Experimental results show that the Bimodal recognition can improve the user recognition rate much more than audio only recognition, especially in the Presence of noise.

• Journal of the Korean Society of Costume
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• v.59 no.1
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• pp.47-59
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• 2009

This paper aimed at the development of face covers which is face protection articles for blocking UV rays and yellow sands, with a view to assist the further activation of outdoor life. The four-stage design process of field study, design, design evaluation, final selection of designs was set up for the effective product development. Field study included on-line and off-line market survey on the products for sale in market and observation on those wearing face covers. Depth interviews were done to know consumer understandings and satisfaction levels for current face covers. The results of market survey showed that most of the face covers currently in the market were mask types, which were produced for the blocking of dust and yellow sands rather than UV ray protection. The major clients of the mask were the women in their 30's-50's and the designs for these clients were very limited. Since the number of younger women and men enjoy the outdoor sports has increased, the face covers for these people should be developed. For this purpose, the direction of new face cover design had 2 points: diversification of design in terms of color, material, shape and giving seasonal differences. 12 designs were designed on the first design stage, but after both wearing test by 16 testees and UV ray blocking test to evaluate these designs, 6 face cover designs were finally selected.