• Journal of the Korean Society of Clothing and Textiles
• /
• v.33 no.10
• /
• pp.1530-1540
• /
• 2009

Life expectancy has increased and people are more concerned with keeping their 'youth'. Appearance is a competitive edge and people are caught up in a baby face fad that has expanded into a culture and beauty trend. This study examined the definitions and phrenological characteristics of a baby face and the causes of the baby face fad in a review of the baby face as a "sociocultural and beauty cultural code" based on literature, journals, previous papers, and Internet materials. Anatomically speaking, a "baby face" refers to a face that keeps the features of a child's face without aging signs. The baby face fad gained momentum due to the influences of the digital culture, the encouragement of the mass media, changes to the social structure, dietary improvements, new living styles, and an evolving aesthetic sense. The results reveal the sociocultural backgrounds behind the "baby face fad" settling down as a part of culture in addition to the phrenological characteristics of a baby face. It is also estimated that the baby face fad could affect the beauty culture and trends as a social phenomena.

• Korean Journal of Human Ecology
• /
• v.14 no.5
• /
• pp.813-820
• /
• 2005

The beauty itself cannot be changed by the time, but the concept of the beauty can be influence by the time and cultural background. The purpose of this study is to analyze the beautiful faces or ugly faces among the young women and to provide useful guideline to make up for the modem concept of beauty. The facial photographs of 180 adlut women(aged between 20 and 29) in Pusan and Ulsan area were sampled to be measured and classified as the beautiful or ordinary or ugly faces. Data were analyzed by Frequencies, Mean, Duncan's Multiple Range Test. The major results were as followings; the Beautiful face has a relatively small face with a broad forehead and a small lower face. It also has a wide palpebral fissure, narrow intercanthal distance, a narrow nose and a big mouth. Physiognomic face length was 182.38mm, the upper face length was 59.82mm, the middle face length 60.82mm, the lower face length 61.76mm, and the index of face length to face breadth was 1.35. And also the faces within the figures are considered as the beautiful or ordinary or ugly faces from those measurements like face length/bizygion breadth, intercanthal distance, mouth width, upper vermilion height, lower vermilion height.

• Proceedings of the Korea Contents Association Conference
• /
• 2003.11a
• /
• pp.223-226
• /
• 2003

When we detect side-view face in color image, we decide a candidate face region using skin-tone color, and confirm to the face by template matching. Cang Wei use a left and a right template of face, calculate to similarity value by hausdorff method, and decide the final side-view face. It has a characteristic that side-view face is wide spreading neck region. To get exactly result, face region is separated vertically by 3 pixel unit, and matched template. In this paper, we assume that a side-view face is a right side-view or a left side-view face. We separate a half of the candidate face region vertically, and regard a left side as left candidate face, a right side as right candidate face by template matching. This method detect faster than Gang Wei method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.2C
• /
• pp.194-199
• /
• 2009

This paper proposes a face detection algorithm that uses geometrical information on face and hair region. This information that face adjoins hair regions can be the important one for face detection. It is also kept in images with frontal, rotated and lateral face. The face candidates are founded by the analysis of skin regions after detecting the skin and hair color regions in an image. Next, the intersected lesions between face candidates and hair's are created. Finally, the face candidates that include the subsets of these regions turn out to be face. Experimental results showed the high detection rates for frontal and lateral faces as well as faces geometrically distorted.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.44 no.1
• /
• pp.102-112
• /
• 2007

This paper includes an analysis of face recognition algorithm to design hardware and presents fixed point model in accordance with it. Face recognition algorithm detects the positions of face and eyes to make use of their feature data to detect and verify human faces. It distinguishes a particular user by means of comparing them with registered face features. To implement the face recognition algorithm into hardware, we developed its fixed point model by analyzing face feature parameters, face acquisition data, and feature detection parameters and operation structure.

• Journal of KIISE:Software and Applications
• /
• v.34 no.10
• /
• pp.945-952
• /
• 2007

The face detection is a necessary first-step in the face recognition systems, with the purpose of localizing and extracting face regions from input images. But it is not a simple problem, because faces have many variations such as scale, rotation and lighting condition. In this paper, we propose a novel method to detect not only frontal faces but also partial rotated faces in still images. Firstly, we produce the eye candidates in the sub-regions of an input image to detect rotated faces. Secondly, the eye candidates are used to measure the angles of rotated faces. Thirdly, we are able to derotate the rotated face then put it to Bayesian classifier. We make an experiment with rotated multiracial face and show the good results in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.748-755
• /
• 2010

In this paper we propose an improved face recognition method using Gabor tensor. Gabor transform is known to be able to represent characteristic feature in face and reduced environmental influence. It may contribute to improve face recognition ratio. We attempted to combine three-dimensional tensor from Gabor transform with MPCA(Multilinear PCA) and LDA. MPCA with tensor which use various features is more effective than traditional one or two dimensional PCA. It is known to be robust to the change of face expression or light. Proposed method is simulated by MATALB9 using ORL and Yale face database. Test result shows that recognition ratio is improved maximum 9~27% compared with exisisting face recognition method.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.2
• /
• pp.31-40
• /
• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.48-52
• /
• 2008

In this paper, we propose a new selective subspace projection method in order to recognize the occlusive face image effectively. The conventional subspace projection method is project to basis image using a full image of face. The face recognition rate has reduced because the face characteristic is easy to be distorted by occlusion. To overcome this problem, the proposed method first decide to occlusion. If it hasn't an occlusion, we get the feature vectors with total basis projection using the conventional subspace projection method. If it has an occlusion, we get one with partial basis projection. We get better recognition rate than conventional PCA and NMF using AR face database with occlusive face images.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.64-69
• /
• 2008

This paper discusses low resolution face recognition using the photon-counting linear discriminant analysis (LDA). The photon-counting LDA asymptotically realizes the Fisher criterion without dimensionality reduction since it does not suffer from the singularity problem of the fisher LDA. The linear discriminant function for optimal projection is determined in high dimensional space to classify unknown objects, thus, it is more efficient in dealing with low resolution facial images as well as conventional face distortions. The simulation results show that the proposed method is superior to Eigen face and Fisher face in terms of the accuracy and false alarm rates.