• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3144-3164
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• 2019

Face recognition systems have several potential applications, such as security and biometric access control. Ongoing research is focused to develop a robust face recognition algorithm that can mimic the human vision system. Face pose, non-uniform illuminations, and low-resolution are main factors that influence the performance of face recognition algorithms. This paper proposes a novel method to handle the aforementioned aspects. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image and later partially uses the PCA to extract mean image. Meanwhile, the AdaBoost and the LDA are used to extract face features. In final stage, classic nearest centre classifier is used for face classification. Proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate and yields much lower error rate for a very challenging situation, such as when only frontal ($0^{\circ}$) face sample is available in gallery and seven poses ($0^{\circ}$, ${\pm}30^{\circ}$, ${\pm}35^{\circ}$, and ${\pm}45^{\circ}$) as a probe on the LFW and the CMU Multi-PIE databases.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.476-486
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• 2007

This paper proposes a resolution enhancement method which can reconstruct high-resolution facial images from single-frame, low-resolution facial images. The proposed method is derived from example-based reconstruction methods and the morphable face model. In order to improve the performance of the example-based reconstruction, we propose the region-based reconstruction method which can maintain the characteristics of local facial regions. Also, in order to use the capability of the morphable face model to face resolution enhancement problems, we define the extended morphable face model in which an extended face is composed of a low-resolution face, its interpolated high-resolution face, and the high-resolution equivalent, and then an extended face is separated by an extended shape vector and an extended texture vector. The encouraging results show that the proposed methods can be used to improve the performance of face recognition systems, particularly to enhance the resolution of facial images captured from visual surveillance systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.141-144
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• 1999

MPEG-4 SNHC FBA(face and body animation) group is going to standardize the MPEG-4 system for low-bit rate communication with the implementation and animation of human body and face on virtual environment. In the first version of MPEG-4 standard, only the face object will be implemented and animated by using FDP (face definition parameter) and FAP(facial animation parameter), which are the abstract parameters of human face for low-bit rate coding. In this paper, MPEG-4 SNHC face object and it's animation were implemented based on the computer graphics tools such as VRML and OpenGL.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.583-586
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• 1999

In this paper, the wavelet transform is performed in the input 256$\times$256 color image and decomposes a image into low-pass and high-pass components. Since the high-pass band contains the components of three directions, edges are detected by combining three parts. After finding the position of face using the histogram of the edge component, a face region in low-pass band is cut off. Since RGB color image is sensitively affected by luminances, the image of low pass component is normalized, and a facial region is detected using face color informations. As the wavelet transform decomposes the detected face region into three layer, the dimension of input image is reduced. In this paper, we use the 3000 images of 10 persons, and KL transform is applied in order to classify face vectors effectively. FCM(Fuzzy C-Means) algorithm classifies face vectors with similar features into the same cluster. In this case, the number of cluster is equal to that of person, and the mean vector of each cluster is used as a codebook. We verify the system performance of the proposed algorithm by the experiments. The recognition rates of learning images and testing image is computed using correlation coefficient and Euclidean distance.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.833-840
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• 2010

In this paper a method for detecting and extracting the face from the image in YCbCr spaceis proposed. The face region is obtained from the complex original image by using the difference method and the face color information is taken from the reduced face region throughthe Ellipse clustering method. The experimental results showed that the proposed method can efficiently detect and extract the face from the original image under the general light intensity except for low luminance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.3
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• pp.121-132
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• 2021

The purpose of this study is to explore ways to improve the quality of real-time video lectures in a non-face-to-face environment using IPA (Importance-Performance Analysis). Recently, due to the impact of COVID-19 in universities, all remote classes are being implemented, so research is needed to raise learner awareness. Accordingly, factor analysis, mean analysis, correspondence analysis, and IPA analysis were performed based on the data of 632 students who responded from March 21 to June 30, 2021 for learners of K University in Chungbuk. First, overall satisfaction was low compared to importance, and the difference in system perception was the largest. Second, the difference in learner perception of real-time video lectures through the IPA matrix showed that the system error and screen cutoff were the largest. Third, the difficulty of lecture content, task and test feedback, etc. are classified. Accordingly, the satisfaction of real-time video lectures in non-face-to-face environments is low, suggesting that school-level support for quality improvement to improve learner satisfaction in non-face-to-face environments and the role of instructors are needed to improve learners' academic achievement.

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.1
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• pp.34-42
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• 2013

Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction's low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• Bulletin of the Korean Chemical Society
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• v.28 no.8
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• pp.1396-1400
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• 2007

In this study, poly(iso-butyl methacrylate-co-ethylene glycol dimethacrylate) [poly(iso-BMA-co-EGDMA)] microgel was prepared and used as a face powder additive. The spreading, adhesiveness, and skin reactivity of poly(iso-BMA-co-EGDMA) microgel-containing face powder II were investigated and compared with the same properties of commercially available Silica bead 700-containing face powder I. In the results, the particle size of the poly(iso-BMA-co-EGDMA) microgel was significantly swelled as a result of sebum absorption. Face powder II showed a lower primary irritation index and a higher adhesiveness than did face powder I. Face powder I showed a low sebum absorption ratio and a relatively high rate of sebum absorption, whereas face powder II, contrastingly, exhibited a high sebum absorption ratio and a low rate of sebum absorption, which properties would reduce the phenomena of facial strain and sliminess. These results indicate that poly(iso- BMA-co-EGDMA) microgel has outstanding sebum absorption characteristic and adhesiveness, and thus that it is a good candidate for use as a face powder additive.