• ETRI Journal
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• v.26 no.6
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• pp.535-544
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• 2004

With video compression standards such as MPEG-4, a transmission error happens in a video-packet basis, rather than in a macroblock basis. In this context, we propose a semantic error prioritization method that determines the size of a video packet based on the importance of its contents. A video packet length is made to be short for an important area such as a facial area in order to reduce the possibility of error accumulation. To facilitate the semantic error prioritization, an efficient hardware algorithm for face tracking is proposed. The increase of hardware complexity is minimal because a motion estimation engine is efficiently re-used for face tracking. Experimental results demonstrate that the facial area is well protected with the proposed scheme.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.95-101
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• 2006

A face detection plays an important role in face recognition, video surveillance, and human computer interaction. In this paper, we propose a progressive threshold method to detect human faces in real time. The consecutive face images are acquired from camera and transformed into YCbCr color space images. The skin color of the input images are separated using a skin color filter in the YCbCr color space and some candidated face areas are decided by connected component analysis. The intensity equalization is performed to avoid the effect of many circumstances and an arbitrary threshold value is applied to get binary images. The eye area can be detected because the area is clearly distinguished from others in the binary image progressive threshold method searches for an optimal eye area by progressively increasing threshold from low values. After progressive thresholding, the eye area is normalized and verified by back propagation algorithm to finalize the face detection.

• Journal of Korean Medical classics
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• v.34 no.3
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• pp.73-83
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• 2021

Objectives : To organize the acupoints used to treat disorders of the head and face in the Zhenjiuzishengjing, and examine their characteristics in application. Methods : 1. The head and face area was divided into 8 parts according to the textbook of meridians and acupoints. Channels belonging to each part were marked. 2. Disorders as mentioned in the Zhenjiuzishengjing were categorized into 8 groups, accordingly. 3. Acupoints used to treat each disorder were organized according to the channels each belonged to. 4. The points were divided according to their proximity, and their application frequency was organized. 5. Based on the organized contents, the characteristics of using proximal and distal points, together with the interrelationship between the channel belonging to the afflicted area and the points locations were examined. Results : In treating disorders in the head and face area, various distal points along with proximal points were suggested in the Zhenjiuzishengjing. In some cases, points belonging to a channel that was irrelevant to the afflicted area were used widely; for proximal points, the Governor/Conception/Triple Energizer/Gallbladder channels were used. For distal points, channels that were related to the Five Zhang were used. Conclusions : Based on the contents of the Zhenjiuzishengjing, the following could be concluded: 1. When treating disorders of the head and face caused by heat, distal points were mostly used. 2. In cases where points which are not part of channels that pass the head or face were used, Zhang disfunction was likely behind such points selection.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.50-57
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• 2005

We present a simple and effective method of face and facial feature detection under pose variation of user face in complex background for the human-robot interaction. Our approach is a flexible method that can be performed in both color and gray facial image and is also feasible for detecting facial features in quasi real-time. Based on the characteristics of the intensity of neighborhood area of facial features, new directional template for facial feature is defined. From applying this template to input facial image, novel edge-like blob map (EBM) with multiple intensity strengths is constructed. Regardless of color information of input image, using this map and conditions for facial characteristics, we show that the locations of face and its features - i.e., two eyes and a mouth-can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlation values with standard facial templates. Experimental results from many color images and well-known gray level face database images authorize the usefulness of proposed algorithm.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2028-2031
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• 2003

This paper proposes a face identification algorithm, robust on lighting condition and complex background. The proposed method estimates facial area under bad light condition by expanding face color boundaries and then finds a lip using the templates for lips. Then the eyes are found using their topological relationship with the long and short axes of lip area. The experimental results have shown that the proposed algorithm is robust on lighting conditions and complex background.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.41-47
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• 1995

This study present a systematic and more economical anthropometric technique to acquire 3-D anthropometric data by the use of moire interferometry, image processing and computer vision techniques. An experiment was performed to measure in anthopometric variables (head and face), such as head length, head breath, length of ear to top of head, contained face areas, etc. We took fourty-five subjects with wide range of ages(18 years to 33 years old). The face area was calculated based on contour information. The results were then compared with plaster bandage methods. It turned out that the proposed method had 90.85% consistancy.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.520-525
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• 2014

Generally, feature area detection methods are widely used for face expression recognition by detecting the feature areas of human eyes, eyebrows and mouth. In this paper, we proposed a face expression recognition method using the histograms of the face, eyes and mouth for many applications including robot technology. The experimental results show that the proposed method has a new type of face expression recognition capability compared to conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.153-156
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• 2007

In this paper, we propose a fast face detection approach using PCA and SVM. In our detection system, first we filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, we use SVM classifier to detect whether there are faces present in the test image. Support Vector Machine (SVM) has great performance in classification task. PCA is used for dimension reduction of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1586-1593
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• 2020

This study was conducted on the face ratio of mammals. It can also be applied to character automation by checking factors about the difference between animal and human face shapes. This paper used the face and face area data generated for Deep Learning learning. In detail, the proportion factors of the area comprising the faces of 20 species of animals and humans were defined and the average ratio was calculated. Next, the proportion of each animal was analyzed using the Principal Component Analysis (PCA). Through this, we would like to propose the golden ratio of mammals.