• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.143-153
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• 2005

The purpose of this research is to study face images according to the difference of facial shape and make-up. A variety of face images can be formulated by computer graphic simulation, combining numerously different facial shapes and make-up styles. In order to check out the diverse images by make-up styles, we applied five forms of eye brows, two types of eye shadows, and three lip shapes to the round-shaped face of a model. The question sheet, used with a operational stimulant in the experiment, contained 28 articles, composed of a pair of bi-ended adjective in 7 point scale. Data were analyzed using Varimax perpendicular rotation method, Duncan's Multiple Range Test, and Three-way ANOVA. After comparing various results of make-up application to various face types, we could find that facial shape, eye-brows, eye-shadow, and lip shapes influence interactively on total facial images. As a result of make-up image perception analyses, a factor structure was divided into mildness, modernness, elegance, and sociableness. Speaking of make-up image in terms of those factors, round form make-up style showed the highest level of mildness. Upward and straight style of make-up had the highest of modernness. Elegance level went highest when eye shadow style was round form and lip style was straight. Lastly, an incurve lip make-up style showed the highest of sociableness.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1310-1319
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• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.179-186
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• 2017

Because facial asymmetry is caused by various causes, the cause analysis is important and quantitative index is needed to the evaluation. In this study, we applied the Nottingham Grading System that was used as a quantitative index to evaluate the facial paralysis by tracking the markers through the image processing and calculating the distance between the markers with images obtained by using the webcam, to evaluate facial asymmetry. The existing Nottingham Grading System has a problem of causing a measurement error in the specific case because the left and right are compared by summing the distance change between the feature points of the face part according to the change of the facial expression. We compared the case of the facial asymmetry and case of normal subject by using the existing Nottingham Grading System and the improved Nottingham grading system. In the existing Nottingham Grading System, case of facial asymmetry and case of facial symmetry were 99.0% and 95.0% respectively in the normal range, but the improved Nottingham Grading System showed facial asymmetry case was 74.0% and facial symmetrical case was 93.2%. The results of experiment show that the improved Nottingham Grading System allows detailed evaluation of each site and improved the problem of the Nottingham Grading System for specific cases.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.7-16
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• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.396-405
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• 2001

Usually it is difficult to extract facial regions in a complex image by using only a predetermined skin color. Expecially, it is more difficult to separate them from background regions that contains the skin color. This paper proposes a face detection method by using gradual range expansion of an initial skin color. By analyzing the skin color distribution several images that are collected in the Web, the range of dense distribution is selected as the range of the initial skin color. In each expanding step, expanded regions in the image are tested whether they can be actual facial regions by using the information of the shape of general face and the location of face organs. The shape of general face is modeled as an ellipse and the aspect ratio of its bounding box is used to define the shape constraint for faces. Only the eyes and lips are used as the face organs, which can be easily detected by extracting horizontal edges in the expanded regions. through several experiments, it is confirmed that the proposed method can detect exactly not only faces having partly distorted regions by highlight but also faces neighboring similar color regions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.897-904
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• 2005

Face Recognition has been an active research area because it is not difficult to acquire face image data and it is applicable in wide range area in real world. Due to the high dimensionality of a face image space, however, it is not easy to process the face images. In this paper, we propose a method to reduce the dimension of the facial data and extract the features from them. It will be solved using the method which extracts the features from holistic face images. The proposed algorithm consists of two parts. The first is the using of principal component analysis (PCA) to transform three dimensional color facial images to one dimensional gray facial images. The second is integrated linear discriminant analusis (PCA+LDA) to prevent the loss of informations in case of performing separated steps. Integrated LDA is integrated algorithm of PCA for reduction of dimension and LDA for discrimination of facial vectors. First, in case of transformation from color image to gray image, PCA(Principal Component Analysis) is performed to enhance the image contrast to raise the recognition rate. Second, integrated LDA(Linear Discriminant Analysis) combines the two steps, namely PCA for dimensionality reduction and LDA for discrimination. It makes possible to describe concise algorithm expression and to prevent the information loss in separate steps. To validate the proposed method, the algorithm is implemented and tested on well controlled face databases.

• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.527-534
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• 2005

The purpose of this research is to study face images according to color of make-up was made by computer graphic simulation. The various facial images can be helpful for choosing suitable make-up color planning. In order to find out the differences of face images by make-up color, three different foundations and seven eye-shadows, six lips were applied on the round face model. Make-up Image Scale was used the scale of seven point modified the S-D method. Data were analyzed by Varimax perpendicular rotation method, Duncan's Multiple Range Test, Three-way ANOVA. As the result of make-up image perception analysis, a factor structure was divided into mildness, modernness, elegance, unique. The factor of mildness, modernness, unique affected on the foundation color. Foundation color was found out to be influential variable to distinguish color perception abilities. Also, the foundation, eye-shadow, lip color were influenced interactively on the perception of elegance factor. Pink color was important color, influenced on the mildness factor. Gray and purple color were influenced on the modernness factor. Mildness factor was perceived as the most bright foundation but unique factor was perceived as the most dark foundation. Then, the foundation, eye-shadow, lip color were influenced interactively on the perception of facial images. The results can be effectively applied to today's marketing and color design management which is focused on the product's emotional image in customer's mind.

• Journal of Physiology & Pathology in Korean Medicine
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• v.29 no.4
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• pp.289-294
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• 2015

In order to make digital processing of facial color, standardization methods of photographing and observational requirements and gloss-detecting are done through preceding papers and actual experiences. Examiner's observational informations should be contained with original and temporary color, normalcy and deviation range and gloss. And these are interrelated with time, interior and exterior temperature, emotional state, so should be recorded too. Picturing procedure should be controlled in simple and practical but objective way. Just water cleansing, 15 to 20 minute resting, prohibiton of moisturizing of examinee are common for examiner. Temperature and moisture, width, light source requirement, brightness, polarizing filter of parlor and camera-to-object distance, posture of examinee are should be recorded. In addition, pre and post-revision of color and manifestation of color space after taking images are needed coping with construction of diagnostic database.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.684-698
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• 2006

This study consists of the stimuli of a female model in her twenties with twenty-two different facial make-up. The subjects of this study are one thousand low hundred ninety seven purposive sampled-male and female grown-ups throughout the country. The period of the research was the December of 2004, one month, and the materials were analyzed by factor analysis, T-examination, analysis of variance, Cronbach's a, Duncan's Multiple Range Test. Here follows the result of the research. Firstly, Familiarity, Intelligence, Fitness, Charm, Tradition and Youth were came out as the result of factor analysis of make-up color image perception. Secondly, in age/lip color perception of bright skin tone, there was difference of Intelligence and Charm. In age/image make-up perception of bright skin tone, there was difference of Familiarity, Charm especially on Cool image make-up. Thirdly in habitant/lip color perception of dark skin tone, there was difference of Intelligence and Charm. In habitant/image make-up perception of bright skin tone, there was difference of Familiarity, Charm and of bright skin tone, Intelligence, Charm, Tradition and Youth. Fourthly, there were the interaction effects on the gender of perceivers and lip color and image make-up of perceivers habitant. Lastly, in preference rate, lip color was more affected by age and image make-up were more affected by perceivers habitant.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.6
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• pp.31-40
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• 2003

Most face recognition systems are based on 2D images and applied in many applications. However, it is difficult to recognize a face when the pose varies severely. Therefore, head pose estimation is an inevitable procedure to improve recognition rate when a face is not frontal. In this paper, we propose a novel head pose estimation algorithm for 3D face recognition. Given the 3D range image of an unknown face as an input, we automatically extract facial feature points based on the face curvature. We propose an Error Compensated Singular Value Decomposition (EC-SVD) method based on the extracted facial feature points. We obtain the initial rotation angle based on the SVD method, and perform a refinement procedure to compensate for remained errors. The proposed algorithm is performed by exploiting the extracted facial features in the normaized 3D face space. In addition, we propose a 3D nearest neighbor classifier in order to select face candidates for 3D face recognition. From simulation results, we proved the efficiency and validity of the proposed algorithm.