• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.12
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• pp.1002-1008
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• 2007

Natural convection flows in a doubly-inclined cubical air-filled cavity are numerically simulated by a solution code(PowerCFD) using unstructured cell-centered method. For a physical realizability, the cavity has one pair of opposing isothermal faces at different temperatures, $T_h\;and\;T_c$, respectively, the remaining four faces having a linear variation from $T_c\;to\;T_h$. The paper redefines a new doubly-inclined orientation for the cubical-cavity benchmark problem. Special attention is paid to three-dimensional thermal characteristics in natural convection according to the new orientation at $Ra=4\times10^4$. Comparisons of the average Nusselt number at the cold face are made with benchmark solutions and experimental results found in the literature. It is found that the average Nusselt number at the cold face has a maximum value at the doubly-inclined angle ranging from $40^{\circ}\;to\; 45^{\circ}$ We also report the effect of new orientation on the type of temperature structure in a doubly-inclined cubical-cavity.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.153-165
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• 2019

A case study of monitoring and analysis of surface settlement induced by tunneling of Tabriz metro line 2 (TML2) is presented in this paper. The TML2 single tunnel has been excavated using earth pressure balanced TBM with a cutting-wheel diameter of 9.49 m since 2015. Presented measurements of surface settlements, were collected during the construction of western part of the project (between west depot and S02 station) where the tunnel was being excavated in sand and silt, below the water table and at an average axis depth of about 16 m. Settlement readings were back-analyzed using Gaussian formula, both in longitudinal and transversal directions, in order to estimate volume loss and settlement trough width factor. In addition to settlements, face support and tail grouting pressures were monitored, providing a comprehensive description of the EPB performance. Using the gap model, volume loss prediction was carried out. Also, COB empirical method for determination of the face pressure was employed in order to compare with field monitored data. Likewise, FE simulation was used in various sections employing the code Simulia ABAQUS, to investigate the efficiency of numerical modelling for the estimating of the tunneling induced-surface settlements under such a geotechnical condition. In this regard, the main aspects of a mechanized excavation were simulated. For the studied sections, numerical simulation is not capable of reproducing the high values of in-situ-measured surface settlements, applying Mohr-Coulomb constitutive law for soil. Based on results, for the mentioned case study, the range of estimated volume loss mostly varies from 0.2% to 0.7%, having an average value of 0.45%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.191-197
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• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.67-73
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• 2010

In this paper, a new face detection method that is more accurate than the conventional methods is proposed. This method utilizes a hybrid of Hausdorff distance based on the geometric similarity between the two sets of points and the LBP distance based on the distribution of local micro texture of an image. The parameters for normalization and the optimal blending factor of the two different metrics were calculated from training sample images. Popularly used face database was used to show that the proposed method is more effective and robust to the variation of the pose, illumination, and back ground than the methods based on the Hausdorff distance or LBP distance. In the particular case, the average error distance between the detected and the true face location was reduced to 47.9% of the result of LBP method, and 22.8% of the result of Hausdorff method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.608-616
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• 2001

This paper proposes a face recognition technique that effectively combines fixed graph matching (FGM) and Fisherface algorithm. EGM as one of dynamic link architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional EGM, the proposed approach could obtain satisfactory results in the perspectives of recognition speeds. Especially, we could get higher average recognition rate of 90.1% than the conventional methods by hold-out method for the experiments with the Yale Face Databases and Olivetti Research Laboratory (ORL) Databases.

• Archives of Plastic Surgery
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• v.37 no.4
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• pp.361-368
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• 2010

Purpose: Authors tried to analyze the influence of individual facial aesthetic subunits on the cognition of facial attractiveness in public and suggest a mathematical model which explain the facial attractiveness. Methods: Independent facial aesthetic subunits are extracted from facial photographs from three women (11 frontal and 7 lateral aesthetic subunits). Each facial subunits of three women are rated in terms of relative rank by 164 peoples (68 man and 96 woman, average age was 32.4, and ranged ${\pm}$ 9.8 years). $x^2$-test and categorical regression analysis were performed. Results: There was no difference in the aesthetic preference in terms of ages or sexes in large. Beautification of individual aesthetic subunits can predict the overall facial attractiveness up to 42.1% in frontal face (Adjusted $R^2$=0.421, F=6.39, p=0.000 < 0.05) and 22.7% in lateral face (Adjusted $R^2$=0.227, F=4.42, p=0.000 < 0.05). Aesthetic appearance of eyes (p=0.001), upper face (p=0.034) in frontal face and midface (p=0.000) in lateral face are statistically important factors in the cognition of facial attractiveness. Conclusion: Authors experimently proved that harmony and balance among facial aesthetic subunits are the most important factors, in embarking on facial aesthetic plastic surgery, for better enhancement of facial attractiveness.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.21-24
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• 2001

In this paper, we present a fast face detection algorithm by estimating the eye region using neural network. To implement a real time face detection system, it is necessary to reduce search space. We limit the search space just to a few pairs of eye candidates. For the selection of them, we first isolate possible eye regions in the fast and robust way by modified histogram equalization. The eye candidates are paired to form an eye pair and each of the eye pair is estimated how close it is to a true eye pair in two aspects : One is how similar the two eye candidates are in shape and the other is how close each of them is to a true eye image A multi-layer perceptron neural network is used to find the eye candidate region's closeness to the true eye image. Just a few best candidates are then verified by eigenfaces. The experimental results show that this approach is fast and reliable. We achieved 94% detection rate with average 0.1 sec Processing time in Pentium III PC in the experiment on 424 gray scale images from MIT, Yale, and Yonsei databases.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.

• Korean Journal of Cognitive Science
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• v.11 no.2
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• pp.53-58
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• 2000

This paper reconstructs local region of a facial expression image from extracted feature points of facial expression image using FCM(Fuzzy C-Meang) clustering algorithm with Gabor wavelets. The feature extraction in a face is two steps. In the first step, we accomplish the edge extraction of main components of face using average value of 2-D Gabor wavelets coefficient histogram of image and in the next step, extract final feature points from the extracted edge information using FCM clustering algorithm. This study presents that the principal components of facial expression images can be reconstructed with only a few feature points extracted from FCM clustering algorithm. It can also be applied to objects recognition as well as facial expressions recognition.