• Journal of Sasang Constitutional Medicine
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• v.19 no.3
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• pp.30-50
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• 2007

1. Objectives Facial features are the important personal characteristics. As a study of all the standardization research about the morphology of face and head, this study was carried out to find the differences according to sex, age. 2. Methods We have collected 1140 cases of patients of the Sasang constitutional Department of Kyung-Hee Medical Center and Kangnam Kyung-Hee Oriental Hospital. we took pictures their frontal view, lateral view of face and measured height, breaths and projected length of face with Face Features Measurement Program(FFMP) and analyzed shape differences of face according to sex, age. 3. Results We got the morphologic characteristics of face according to sex, age. And we will upgrade the Sasangin Classification System by this Morphologic study of Head and Face. 4. Conclusions 1) The morphologic characteristics of face according to sex (1) A man's head is longer and wider than a woman's in most measurement. (2) A woman's eye is bigger in height than man's. (3) A woman's face is looked full-filled than man's. (4) A man's polion is higher than pupil but a woman's polion is lower than pupil. 2) The morphologic characteristics of face according to age (1) Young age's forehead is longer and higher than old age's. (2) Young age's eyebrow is wider than old age's (3) Young age's eye is bigger in height than old age's (4) Old age's nose is longer, wider and lower than young age's (5) Old age's ear is longer than young age's (6) Old age's bigonial breath is wider than young age's (7) Old age's low face is extinguished than you age's

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.392-409
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• 2016

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.329-335
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• 2023

Perceived age is defined as age estimated based on physical appearance. Perceived age is an important indicator of the overall health status of the elderly. This is because people who appear older tend to have higher rates of morbidity and mortality than people of the same chronological age. Although perceived age is an important indicator, there is a lack of objective methods to quantify perceived age. In this paper, we construct a quantified perceived age model from face images using a convolutional neural network. The face images are enlarged to super-resolution and the skin, an important feature in perceived age, is made clear. Moreover, through Tanh-polar transformation, the central area of the face occupies a relatively larger area than the boundary area, helping the neural network better recognize facial skin features. The experimental results show mean absolute error (MAE) of 6.59, showing that the proposed model is superior to existing method.

• Journal of the Korean Society of Costume
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• v.57 no.5 s.114
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• pp.151-165
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• 2007

Appearance matters in society today. Women want to feel good and look their best. They do make-up, wear garment and accessory for their good looking. Doing make-up, we have to know how we are look and to consider face shape. But it is difficult to recognize face shape. Because there is no standard face shape of adult women of quantitative analysis. The purpose of this study was to offer standard face shape of adult women in Korea. Furthermore, the study was to determine and differentiate face shape of each age group to set the basic data for the Korean beauty industry. In this study, photographs of 600 Korean women, age between $20{\sim}50's$, were indirectly measured in Venus face2D program. The measurements were analyzed by statistical methods. As a result of basic statistical data analysis, the average lengths of face were 196mm, lengths of forehead-hairline between eyebrows were 62mm, lengths of eyebrow between noses were 68mm, length of nose between chin were 66mm, and width of face were 150mm. By comparing to each age group's face using ANOVA, the statistically noticeable differences were found in measurements.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.203-211
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• 2020

This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

• IE interfaces
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• v.15 no.2
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• pp.199-208
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• 2002

This study was performed to measure the various dimensions of the head and face for Korean adults. Three hundred and eighteen males and two hundred and sixty females, age ranged 18 to 60, participated for this study. Thirty-six dimensions were selected to measure. Subjects were divided into three age groups - 18 to 29, 30 to 39, and 40 to 60 - for each sex. The data were analyzed ta see the differences between the age groups and sex using SAS program. Also, the results of this study were compared to the data of Japanese and US. army. The results showed that the 'ear length', 'bigonial breadth' and 'bitragion submandibular arc' increased as the age increased(p<0.01). However, not much of differences were shown between the age groups in most of other dimensions. Males were significantly bigger than females in every dimensions. The comparison between Korea and Japanese showed significant differences in many dimensions. Due to this reason, it is considered that more caution has to be exercised in using Japanese data for the Korean. The Americans showed to be significantly bigger than Korean in most dimensions. It showed that Koreans have more roundish face and wider nose ridge than Americans. The results of this study can be used to design the products that related to the head and face.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.39-44
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• 2010

In this paper, we addressed a face transform scheme with age-progressing based on vector representation. Proposed approach utilized a vector modeling as well as morphing so as to improve not only a reliability but also a consistency. For the more, some elements of texture change owing to the face shape are defined and some parameters with respect to the internal and external environments are also considered. To testify the proposed approach, estimation of similarity is performed with qualitative manner by using experimental output, and finally resulted in satisfactory for face shape transformation aged from sixty to fourteen.

• Korean Journal of Oriental Medicine
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• v.6 no.1
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• pp.29-46
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• 2000

The clinical application of constitutional Diagnosis is the most important part of Sasang constitutional medicine. It has been studied in various way. The study of morphologic characteristics on the head and face has been identified but didn't considered the Variations of age and sex. For the statistic analysis of the correlation between the sasang constitution and the shape of the face, the head-facial part of 182 cases(the group of throughout the age) and 69 cases(the group of age 30 to 40) were measured by Martin's measurement and analysis of a) the measurement value of height and the component ratio from the Gnathion to each part of face by constitution. b) the measurement value of depth and the component ratio from T-projected to each part of the face by constitution. c) the measurement value of breadth and component ratio between each parts of the facial breadth by constitution. d) the characteristics on each part of face by constitution. e) the result of discriminant analysis about the constitution Authors obtained the results from the study as follows: 1. Taeum-In group is characterized by the value of variables had a tendency to maximum value in throughout the age, the charateristics that the total group show is the shape of face is wide shape in horizontally and flat, the nasal breadth is wider than other constitutions, the lips is narrower than Soyang-In in horizontally and thick shape in vertically, and the biogonial breadth is more developed than other constitutions, so lower face is developed. The charateristics that only the age of 30 40 group show is that the lips is thicker than Soeum-IN. The projection of inter-eyebrows is more projected than Soyang-IN. 2. Soeum-IN group is characterized by the value of variables had a tendency to minimum value in throughout the age. The characteristics that the total group show is the lips is thiner than other constitutions, the breadth of eyes is wider than other constitutions, the difference between sellion and nasal breadth is to be little. The charateristics that only the age of 30 40 group show is that the ratio of upper face in physiognomic face is lager than Soyang-IN. 3. The total age group of Soyang-In is characterized by the shape of face is long in vertically and narrow in horizontally, the total eyebrow breadth and lips and philtrun is wider than other constitutions. The charateristics that only the age of 30 40 group is characterized by the projection of sellion is more projected than Taeum-IN. 4. The values which showed significance in both age group V76, V52, V54, V55, V57, V59, V64, V65, V67, V88, V89, V148, V150, V151, V155, V160, V161, V28, V50, V99, V102, V167, V169, V173, V175, V177, V181 are 27 in all. 5. The values which was significant in the age 30 to 40 group V77, V78, V79, V109, V140, V142, V143, V166, V174 are 9 in all.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.151-157
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• 2012

Recently, owing to the technology advances in computer science and image processing, age of face classification have become prevalent topics. It is difficult to estimate age of facial shape with statistical figures because facial shape of the person should change due to not only biological gene but also personal habits. In this paper, we proposed a robust age of face classification method by using Gabor feature and fuzzy support vector machine(SVM). Gabor wavelet function is used for extracting facial feature vector and in order to solve the intrinsic age ambiguity problem, a fuzzy support vector machine(FSVM) is introduced. By utilizing the FSVM age membership functions is defined. Some experiments have conducted to testify the proposed approach and experimental results showed that the proposed method can achieve better age of face classification precision.

• The International Journal of Costume Culture
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• v.6 no.2
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• pp.93-104
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• 2003

The purpose of this study was to investigate the relationship between women's face satisfaction and makeup satisfaction, to disclose the differences of makeup satisfaction according to demographic variables, and to examine how makeup satisfaction was influenced by face satisfaction and demographic variables. The subjects were 200 women over age 17 living in Seoul and its peripheral areas. The results of this study were as follows: Face satisfaction were drawn three factors. Factor 1 was face contour satisfaction, Factor 2 was skin satisfaction, and Factor 3 was lips and eyes satisfaction. There were significant positive relationship between factors of face satisfaction and makeup satisfaction. Also, the face contour satisfaction was in positive correlation with satisfaction of features, and the skin satisfaction was in positive correlation with that of features. There were significant positive correlations between makeup satisfaction and face shape, eyes, nose, lips, chin, and cheek bone satisfaction. Face satisfaction didn't show significant difference according to demographic variables, but makeup satisfaction showed significant difference according to age and occupation. Face satisfaction was influenced by the facial face, clarity of skin, elasticity of skin, skin color, and ages. The explanatory power of the 4 variables were 24.5%. Makeup satisfaction was influenced by lips and eyes satisfaction, ages, and skin care level. The explanatory power of the 3 variables were 13.3%.