• Journal of Game and Entertainment
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• v.2 no.2
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• pp.45-50
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• 2006

In a face detection system based on template matching, histogram equalization or log transform is applied to an input image for the intensity normalization and the image improvement. It is known that they are noneffective in improving an image with intensity distortion by illumination variation. In this paper, we propose an efficient image improvement method called as a bilateral symmetry average for images with intensity distortion by illumination variation. Experimental results show that our method delivers the detection performance better than previous methods and also remarkably reduces the number of face candidates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• International Journal of Contents
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• v.9 no.3
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• pp.9-14
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• 2013

In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.162-173
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• 2022

In non-face-to-face teaching-learning, a survey was conducted on 55 students in the department of chemistry education at university A on the variables (behavioral control, instructor-learner interaction, cognitive learning) affecting learning satisfaction and academic achievement. There were relatively large positive correlations between variables. The positive correlation between them was found to be the factors that influenced learning satisfaction and academic achievement in non-face-to-face teaching-learning. The average values of non-face-to-face teaching-learning for each variable were lower than the corresponding values of face-to-face teaching-learning, respectively. As a result of the perception survey on the detailed factors of each variable, negative responses were relatively high in factors such as 'concentration of behavior' in behavioral control, 'level-considered explanation' in instructor-learner interaction, and 'knowledge understanding' in cognitive learning.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.34-40
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• 2006

1. Objectives We are going to develope 3D Face Recognition Apparatus to analyse the facial characteristics of the Sasangin. In the process, we should identify the recognition rate of the three dimensional position using this Apparatus. 2. Methods We took a photograph of calibrator($280{\times}400mm$) with interval of 20mm longitudinal direction of 10 times using 3D Face Recognition Apparatus. In the practice, we obtained 967 point to the exclusion of points deviating from the visual field of dual camera. And we made a comparison between measurement values and three dimensional standard values to calculate the errors. 3. Results and Conclusions In this test, the average error rate of X axis values was 0.019% and the maximum error rate of X axis values was 0.033%, the average error rate of Y axis values was 0.025% and the maximum error rate of Y axis values was 0.044%, the average error rate of Z axis values was 0.158% and the maximum error rate of Z axis values was 0.269%. This results exhibit much improvement upon the average error rate 1% and the maximum error rate 2.242% of the existing 3D Recognition Apparatus. In conclusion, we assessed that this apparatus was adaptable to abstract the facial characteristic point from three dimensional face shape in the mechanical aspects.

• Science of Emotion and Sensibility
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• v.7 no.1
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• pp.23-28
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• 2004

In this paper, we investigated the affective components of facial beauty. In study 1, we did factor analysis of affective evaluations of the faces, and about 65% of the variances are explained by only two factors. Two factors were named 'sharp' and 'soft', respectively. In study 2, the correlation between facial beauty and affective evaluations was analyzed, and the correlation between facial beauty and sharp factor was significant. In study 3, we made the new images by morphing and warping the faces: 'average', 'high-ranked', and 'exaggerated'. The participants evaluated the 'high-ranked' face more beautiful than the 'average' face, and the 'exaggerated' face more beautiful than the 'high-ranked' face. The rating of affective words on the faces showed that the 'average' face was related to 'soft' impression, the 'high-ranked' image to 'sharp' impression, and the 'exaggerated' face might have double impression. These results might support the directional hypothesis for the facial beauty.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.313-316
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• 2003

This paper proposes a new measurement method of face region size for real-time serveillance systems. The proposed method consists of three steps. In the first step, it detects global face area based on the block-based difference images. In the second step, it measures the face region size using face color information. In the third step, it estimates the face position in the next input image using the trajectory of face regions. The experimental results have shown that the proposed algorithm measures the face size within 20％ relative error on average, which is a]towable for most surveillance systems.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.91-98
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• 2019

Many researchers have attempted to recognize three-dimensional faces using feature points extracted from two-dimensional facial photographs. However, due to the limit of flat photographs, it is very difficult to recognize faces rotated more than 15 degrees from original feature points extracted from the photographs. As such, it is difficult to create an algorithm to recognize faces in multiple angles. In this paper, it is proposed a new algorithm to recognize three-dimensional face recognition based on feature points extracted from a flat photograph. This method divides into six feature point vector zones on the face. Then, the vector value is compressed and expanded according to the rotation angle of the face to recognize the feature points of the face in a three-dimensional form. For this purpose, the average of the compressibility and the expansion rate of the face data of 100 persons by angle and face zone were obtained, and the face angle was estimated by calculating the distance between the middle of the forehead and the tail of the eye. As a result, very improved recognition performance was obtained at 30 degrees of rotated face angle.