• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1827-1835
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• 2010

In this paper, facial feature areas in user picture are created by facial feature points extracted by ASM(Active Shape Model). In a existing virtual make-up application, users manually select a few features that are exactly. Users are uncomfortable with this method. We propose a virtual makeup application using ASM that does not require user input. In order to express a natural makeup, the modified alpha blendings for each cosmetic are used to blend skin color with cosmetic color. The Virtual makeup application was implemented to apply Foundation, Blush, Lip Stick, Lip Liner, Eye Pencil, Eye Liner and Eye Shadow.

• Archives of Craniofacial Surgery
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• v.20 no.1
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• pp.3-9
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• 2019

Facial nerve palsy has an effect on a person's well-being functionally and psychologically. Therefore, comprehensive patient management is essential. One of the most common uncomfortable and potentially debilitating features is associated with the incapacity for eye closure. Restoration of eye closure is a key consideration during the surgical management of facial palsy. In this article, we introduce simple surgical methods-which are relatively easy to learn and involve the upper and lower eyelids-for achieving eye closure. Correcting upper eyelid function involves facilitating the component of eye closure that is in the same direction as gravity and is, therefore, less complicated and favorable outcomes than correction of lower lid. Aesthetic aspects should be considered to correct the asymmetry caused by facial palsy. Lower eyelid function involves a force that opposes gravity for eye closure, which makes correction of lower eyelid ectropion more challenging than surgery for the upper eyelid, particularly in terms of effecting a sustained correction. Initially, proper ophthalmic evaluation is required, including identifying the chronicity and severity of ectropion. Also, it is important to determine whether or not lateral canthoplasty is necessary. The lateral tarsal strip procedure is commonly used for lower lid correction. However, effective lower lid correction can be achieved with better cosmesis when extensive supporting techniques are applied, including those involving cheek tissue.

• Korean Journal of Cognitive Science
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• v.13 no.3
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• pp.1-10
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• 2002

This study was performed to analyze the physical attributes of the faces and affective words on the fares. For analyzing physical attributes inside of a face, 36 facial features were selected and almost of them were the lengths or distance values. For analyzing facial contour 14 points were selected and the lengths from nose-end to them were measured. The values of these features except ratio values normalized by facial vortical length or facial horizontal length because the face size of each person is different. The principal component analysis (PCA) was performed and four major factors were extracted: 'facial contour' component, 'vortical length of eye' component, 'facial width' component, 'eyebrow region' component. We supposed the five-dimensional imaginary space of faces using factor scores of PCA, and selected representative faces evenly in this space. On the other hand, the affective words on faces were collected from magazines and through surveys. The factor analysis and multidimensional scaling method were performed and two orthogonal dimensions for the affections on faces were suggested: babyish-mature and sharp-soft.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.75-83
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• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.143-153
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• 2006

Facial feature detection is a basic technology in applications such as human computer interface, face recognition, face tracking and image database management. The speed of feature detection algorithm is one of the main issues for facial feature detection in real-time environment. Primary factors like a variation by lighting effect, location, rotation and complex background give an effect to decrease a detection ratio. A facial feature detection algorithm is proposed to improve the detection ratio and the detection speed. The proposed algorithm detects skin regions over the entire image improved by CLAHE, an algorithm for light compensation against varying lighting conditions. To extract facial feature points on detected skin regions, it uses appearance-based geometrical characteristics of a face. Since the method shows fast detection speed as well as efficient face-detection ratio, it can be applied in real-time application to face tracking and face recognition.

• Archives of Plastic Surgery
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• v.48 no.5
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• pp.503-510
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• 2021

Facial feminization surgery (FFS) incorporates aesthetic and craniofacial surgical principles and techniques to feminize masculine facial features and facilitate gender transitioning. A detailed understanding of the defining male and female facial characteristics is essential for success. In this first part of a two-part series, we discuss key aspects of the general preoperative consultation that should be considered when evaluating the prospective facial feminization patient. Assessment of the forehead, orbits, hairline, eyebrows, eyes, and nose and the associated procedures, including scalp advancement, supraorbital rim reduction, setback of the anterior table of the frontal sinus, rhinoplasty, and soft tissue modifications of the upper and midface are discussed. In the second part of this series, bony manipulation of the midface, mandible, and chin, as well as soft tissue modification of the nasolabial complex and chondrolaryngoplasty are discussed. Finally, a review of the literature on patient-reported outcomes in this population following FFS is provided.

• Archives of Craniofacial Surgery
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• v.22 no.2
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• pp.78-84
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• 2021

Background: Facial feminization surgery and malarplasty require information concerning facial features in the malar area. Such information varies as a function of sex and race. The objectives of this study aimed to quantitatively evaluate the location of malar prominence across sexes in the Southeast Asian population, and identify sex-specific differences in malar prominence using a combination of two-dimensional (2D) computed tomography (CT) and three-dimensional (3D) CT. Methods: The location of malar prominence was evaluated in 101 Thai adults, consisting of 52 men and 49 women. This study used both 2D CT and 3D CT to achieve greater accuracy, in which 2D CT was used to measure malar distance, malar summit width, facial width, and malar summitto-facial width ratio whereas 3D CT was used to evaluate the positional relationship between the zygomatic summit and four reference points of the zygoma. Results: The malar summit was positioned more laterally in males (p< 0.01) and was more projected in females (p= 0.01). The other 2D-parameters were wider in males. The ratio between the malar summit width and facial width showed similar results for both sexes. The vertical dimension did not show any statistically significant differences; however, a higher summit position was observed in males. Conclusion: The zygomatic summit is positioned more laterally in males and is more projected in females. However, the ratio was similar, which indicates that the male cranium is larger in size. Based on the results in this study, when facial feminization surgery or malarplasty is performed on a Southeast Asian patient, the malar bone should be reduced horizontally and moved forward for better outcomes.

• Journal of Sasang Constitutional Medicine
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• v.34 no.3
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• pp.31-40
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• 2022

Objectives Sasang constitutional medicine is a traditional Korean medicine that classifies humans into four constitutions in consideration of individual differences in physical, psychological, and physiological characteristics. In this paper, we proposed a method to classify Taeeum person (TE) and Non-Taeeum person (NTE), Soeum person (SE) and Non-Soeum person (NSE), and Soyang person (ST) and Non-Soyang person (NSY) using a convolutional neural network with only facial images. Methods Based on the convolutional neural network VGG16 architecture, transfer learning is carried out on the facial images of 3738 subjects to classify TE and NTE, SE and NSE, and SY and NSY. Data augmentation techniques are used to increase classification performance. Results The classification performance of TE and NTE, SE and NSE, and SY and NSY was 77.24%, 85.17%, and 80.18% by F1 score and 80.02%, 85.96%, and 72.76% by Precision-Recall AUC (Area Under the receiver operating characteristic Curve) respectively. Conclusions It was found that Soeum person had the most heterogeneous facial features as it had the best classification performance compared to the rest of the constitution, followed by Taeeum person and Soyang person. The experimental results showed that there is a possibility to classify constitutions only with facial images. The performance is expected to increase with additional data such as BMI or personality questionnaire.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.