• Journal of the Korean Society of Food Culture
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• v.16 no.2
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• pp.109-117
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• 2001

Judging from the studies carried out by Dr. Jo, Yong Jin on the Koreans' faces, Koreans divided into two constitutions according to their facial features and heritages. The one population is the Northern lineage whose ancestor migrated from Siberia in ice age. In order to survive in cold climate, they have developed a high level of metabolic heat production. Cold adaptation for preventing heat loss results in a reduction in the facial surface area with small eyes, nose and lips. The other population is the Southern lineage who is the descent of native in Korean peninsular. They have big eyes with double edged eyelids, broad nose and thick lips. It is generally believed that both genetic and environmetal factors influence eating behaviors. Although we can't recognized their heritage that may contribute to the metabolism and eating behavior, we commonly recognize their physiological heritage acceding to their facial features. In order to investigate the relationship among the size and shape of facial feature, the eating behavior, anthropometric measurement in female college students, the eating behaviors was measured during an instant-noodle lunch eaten in a laboratory setting at the ambient temperature of $23^{\circ}C$. The anterior surface area of left eye and length of right eye were positively correlated with the difference between the peak postprandial and the meal-start core temperature. The surface area of lower lip also negatively correlated with the meal-start core temperature and meal duration. In addition, the total lips' area was positively correlated with the difference between the peak postprandial and the meal-start core temperature and negatively correlated with the meal duration. However anthropometric measurements were not related with the size of facial features.

• Journal of Internet Computing and Services
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• v.6 no.4
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• pp.93-102
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• 2005

In Recent times, Netizens have frequently use virtual character 'Avatar' schemes in order to present their own identity, there is a strong need for avatars to resemble the user. This paper proposes an extraction technique for facial region and features that are used in generating the avatar automatically. For extraction of facial feature component, the method uses ACM and edge information. Also, in the extraction process of facial region, the proposed method reduces the effect of lights and poor image quality on low resolution pictures. this is achieved by using the variation of facial area size which is employed for external energy of ACM. Our experiments show that the success rate of extracting facial regions is $92{\%}$ and accuracy rate of extracting facial feature components is $83.4{\%}$, our results provide good evidence that the suggested method can extract the facial regions and features accurately, moreover this technique can be used in the process of handling features according to the pattern parts of automatic avatar generation system in the near future.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.207-211
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• 2011

This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images. Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest, especially on the local features on the face. This is a very unique attribute needed to extract special features around the facial components like eyebrows, eyes, mouth and nose. The Covariance matrices computed on Gabor filtered faces are adopted as the feature representation for face recognition. Geodesic distance measure is used as a matching measure and is preferred for its global consistency over other methods. Geodesic measure takes into consideration the position of the data points in addition to the geometric structure of given face images. The proposed method is invariant and robust under rotation, pose, or boundary distortion. Tests run on random images and also on publicly available JAFFE and FRAV3D face recognition databases provide impressively high percentage of recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.4-100
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• 2002

For visual navigation, mobile robot can use a stereo camera which has large field of view. In this paper, we propose an algorithm to detect and recognize human face on the basis of such camera system. In this paper, a new coarse to fine detection algorithm is proposed. For coarse detection, nearly face-like areas are found in entire image using dual ellipse templates. And, detailed alignment of facial outline and features is performed on the basis of view- based multiple appearance model. Because it hard to finely align with facial features in this case, we try to find most resembled face image area is selected from multiple face appearances using most distinguished facial features- two eye...

• 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 KIEE Conference
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• 2004.05a
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.556-570
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• 2021

The existing video expression recognition methods mainly focus on the spatial feature extraction of video expression images, but tend to ignore the dynamic features of video sequences. To solve this problem, a multi-mode convolution neural network method is proposed to effectively improve the performance of facial expression recognition in video. Firstly, OpenFace 2.0 is used to detect face images in video, and two deep convolution neural networks are used to extract spatiotemporal expression features. Furthermore, spatial convolution neural network is used to extract the spatial information features of each static expression image, and the dynamic information feature is extracted from the optical flow information of multiple expression images based on temporal convolution neural network. Then, the spatiotemporal features learned by the two deep convolution neural networks are fused by multiplication. Finally, the fused features are input into support vector machine to realize the facial expression classification. Experimental results show that the recognition accuracy of the proposed method can reach 64.57% and 60.89%, respectively on RML and Baum-ls datasets. It is better than that of other contrast methods.

• Journal of Korean Neurosurgical Society
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• v.55 no.5
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• pp.244-247
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• 2014

Objective : The aim of this study was to evaluate the microanatomy and histological features of the central myelin in the root exit zone of facial nerve. Methods : Forty facial nerves with brain stem were obtained from 20 formalin fixed cadavers. Among them 17 facial nerves were ruined during preparation and 23 root entry zone (REZ) of facial nerves could be examined. The length of medial REZ, from detach point of facial nerve at the brain stem to transitional area, and the thickness of glial membrane of central myelin was measured. We cut brain stem along the facial nerve and made a tissue block of facial nerve REZ. Each tissue block was embedded with paraffin and serially sectioned. Slices were stained with hematoxylin and eosin (H&E), periodic acid-Schiff, and glial fibrillary acid protein. Microscopy was used to measure the extent of central myelin and thickness of outer glial membrane of central myelin. Thickness of glial membrane was examined at two different points, the thickest area of proximal and distal REZ. Results : Special stain with PAS and GFAP could be differentiated the central and peripheral myelin of facial nerve. The length of medial REZ was mean 2.6 mm (1.6-3.5 mm). The glial limiting membrane of brain stem is continued to the end of central myelin. We called it glial sheath of REZ. The thickness of glial sheath was mean $66.5{\mu}m(40-110{\mu}m$) at proximal REZ and $7.4{\mu}m(5-10{\mu}m$) at distal REZ. Conclusion : Medial REZ of facial nerve is mean 2.6 mm in length and covered by glial sheath continued from glial limiting membrane of brain stem. Glial sheath of central myelin tends to become thin toward transitional zone.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.505-509
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• 2011

This paper attempts to classify known facial expressions and to establish the correlations between two regions (eye + eyebrows and mouth) in identifying the six prototypic expressions. Covariance is used to describe region texture that captures facial features for classification. The texture captured exhibit the pattern observed during the execution of particular expressions. Feature matching is done by simple distance measure between the probe and the modeled representations of eye and mouth components. We target JAFFE database in this experiment to validate our claim. A high classification rate is observed from the mouth component and the correlation between the two (eye and mouth) components. Eye component exhibits a lower classification rate if used independently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.373-376
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• 2013

Nowadays, eyes detection is required and considered as the most important step in several applications, such as eye tracking, face identification and recognition, facial expression analysis and iris detection. This paper presents the eyes detection in facial images using Harris corner detection. Firstly, Haar-like features for face detection is used to detect a face region in an image. To separate the region of the eyes from a whole face region, the projection function is applied in this paper. At the last step, Harris corner detection is used to detect the eyes location. In experimental results, the eyes location on both grayscale and color facial images were detected accurately and effectively.