• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• The korean journal of orthodontics
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• v.25 no.1 s.48
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• pp.111-128
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• 1995

This study was investigated to assess the difference of facial height and occlusal plane inclination between pre- and post-treatment in Class I malocclusion group The subjects consisted of 35 extraction patients and 30 nonextraction patient,;, and was subdivided into Group I(overbite<0mm), Group II(04mm) in reference to overbite, and adolescent group and adult group in reference to age. Lateral cephalogram was taken with standard method, traced, and digitized for each subject. The computerized statistical analysis was carried out with SAS program The results wolf as follows. 1. In both groups of extraction and nonextraction group the anterior facial height increased after orthodontic treatment but there was no significant difference(p>0.05) between each goup. 2. There was no statstical significance in change of occlusal plane inclination in adolescent group, but significant difference(p<0.05) among three subgroup in adult group. 3. In adolescent-extraction and adolescent-nonextraction group there was significant increase of anterior facial height and posterior facial height, and was superior to adult groups in posterior facial height increment. 4 In all groups upper and lower molars were uprighted to occlusal plane. This had statistically significant effect.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of KIISE:Software and Applications
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• v.28 no.12
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• pp.947-954
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• 2001

In this paper, we propose a method for the recognition of various head gestures with automata technique applied to the sequence of facial pose states. Facial regions as detected by using the optimum facial color of I-component in YIQ model and the difference of images adaptively selected. And eye regions are extracted by using Sobel operator, projection, and the geometric location of eyes Hierarchical feature analysis is used to classify facial states, and automata technique is applied to the sequence of facial pose states to recognize 13 gestures: Gaze Upward, Downward, Left ward, Rightward, Forward, Backward Left Wink Right Wink Left Double Wink, Left Double Wink , Right Double Wink Yes, and No As an experimental result with total 1,488 frames acquired from 8 persons, it shows 99.3% extraction rate for facial regions, 95.3% extraction rate for eye regions 94.1% recognition rate for facial states and finally 99.3% recognition rate for head gestures. .

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.7-15
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• 2014

This paper proposed a recognition algorithm of human facial expressions using the PCA and the template matching. Firstly, face image is acquired using the Haar-like feature mask from an input image. The face image is divided into two images. One is the upper image including eye and eyebrow. The other is the lower image including mouth and jaw. The extraction of facial components, such as eye and mouth, begins getting eye image and mouth image. Then an eigenface is produced by the PCA training process with learning images. An eigeneye and an eigenmouth are produced from the eigenface. The eye image is obtained by the template matching the upper image with the eigeneye, and the mouth image is obtained by the template matching the lower image with the eigenmouth. The face recognition uses geometrical properties of the eye and mouth. The simulation results show that the proposed method has superior extraction ratio rather than previous results; the extraction ratio of mouth image is particularly reached to 99%. The face recognition system using the proposed method shows that recognition ratio is greater than 80% about three facial expressions, which are fright, being angered, happiness.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.633-638
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• 2005

The extraction of a face is a very important part for human interface, biometrics and security. In this paper, we applies DCM(Dilation of Color and Motion) filter and Active Contour Models to extract facial outline. First, DCM filter is made by applying morphology dilation to the combination of facial color image and differential image applied by dilation previously. This filter is used to remove complex background and to detect facial outline. Because Active Contour Models receive a large effect according to initial curves, we calculate rotational degree using geometric ratio of face, eyes and mouth. We use edgeness and intensity as an image energy, in order to extract outline in the area of weak edge. We acquire various head-pose images with both eyes from five persons in inner space with complex background. As an experimental result with total 125 images gathered by 25 per person, it shows that average extraction rate of facial outline is 98.1% and average processing time is 0.2sec.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.591-611
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• 2016

High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.126-132
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• 2000

This paper presents a facial expression recognition based on Gabor wavelets that uses a fuzzy C-means(FCM) clustering algorithm and neural network. Features of facial expressions are extracted to two steps. In the first step, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image's 2-D Gabor wavelet coefficient histogram. In the next step, we extract sparse features of facial expressions from the extracted edge information using FCM clustering algorithm. The result of facial expression recognition is compared with dimensional values of internal stated derived from semantic ratings of words related to emotion. The dimensional model can recognize not only six facial expressions related to Ekman's basic emotions, but also expressions of various internal states.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.280-286
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• 2022

Facial recognition system is a biometric manipulation. Its applicability is simpler, and its work range is broader than fingerprints, iris scans, signatures, etc. The system utilizes two technologies, such as face detection and recognition. This study aims to develop a facial recognition system to recognize person's faces. Facial recognition system can map facial characteristics from photos or videos and compare the information with a given facial database to find a match, which helps identify a face. The proposed system can assist in face recognition. The developed system records several images, processes recorded images, checks for any match in the database, and returns the result. The developed technology can recognize multiple faces in live recordings.