• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.2
• /
• pp.31-40
• /
• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• Journal of Korea Multimedia Society
• /
• v.10 no.5
• /
• pp.594-603
• /
• 2007

This paper proposes a watermarking scheme for 3D CAD design drawing. In the proposed scheme, we embed binary watermarks into line, arc, and polygon face components that are the basic component of 3D CAD design drawing. The embedding target component can be selected randomly among three components or by the component distribution in drawing. In line components, a watermark bit is embedded into the ratio of the length of a target line and an average length of lines that are connected into a target line. Furthermore, a watermark bit is embedded into a curvature radius on the basis of a center point in a arc component and also is embedded into a ratio of two sides in a polygonal face component. Experimental results verified that the proposed watermarking has the robustness against Format conversion, rotation translation, scaling, cropping, and layer cutting and also SNR of watermarked component is about 39.89-42.50 dB.

• Journal of the Korean Society for Precision Engineering
• /
• v.3 no.4
• /
• pp.30-42
• /
• 1986

On the analysis of cutting mechanics in orthogonal cutting, each cutting force component can be predicted. By adding the flank face wear term to the prediction equation for cutting force components, complete equations are obtained. Using these equations, it is shown that cutting force components are increased linearly as flank face wear land is developed, in theory and experiment. By making non-dimensional term ie. Fv/Fc, the width of variation of output signal Fv/Fc is greately decreased compared with each cutting force component as cutting condition is varied. Among these conditions, the variation of chip width in the range of more than 1mm and that of cutting velocity have little effect on the output signal Fv/Fc, that of Flank face werr land can be detected without difficulty.

• Journal of KIISE:Software and Applications
• /
• v.32 no.12
• /
• pp.1247-1259
• /
• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.1
• /
• pp.25-31
• /
• 2005

In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.583-586
• /
• 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
• /
• v.9 no.1
• /
• pp.11-22
• /
• 2006

Recently face detection has actively been researched due to its wide range of applications, such as personal identification and security systems. In this paper, a new face detection method based on the distribution map is proposed. Face-like regions are first extracted by applying the skin color map with the frequency to a color image and then, possible eye regions are determined by using the pupil color distribution map within the face-like regions. This enables the reduction of space for finding facial features. Eye candidates are detected by means of a template matching method using weighted window, which utilizes the correlation values of the luminance component and chrominance components as feature vectors. Finally, a cost function for mouth detection and location information between the facial features are applied to each pair of the eye candidates for face detection. Experimental results show that the proposed method can achieve a high performance.

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.5
• /
• pp.43-51
• /
• 2009

In this paper, we propose an efficient method of face retrieval by using PCA(principal component analysis) based features. The coarse-to-fine strategy is adopted to sort the retrieval results in the lower dimensional eigenface space and to rearrange candidates at high ranks in higher dimensional eigenface space. To evaluate similarity between a query face image and class reference image, we utilize the PD (projection distance), MQDF(modified quadratic distance function) and MED(minimum Euclidean distance). The experimental results show that the proposed method which rearrange the retrieval results incrementally by using projection distance is efficient for face image retrieval.

• Proceeding of Spring/Autumn Annual Conference of KHA
• /
• 2009.04a
• /
• pp.230-235
• /
• 2009

In our country, the application of the design method of face to face dimension in wall-slab apartment has passed 10 years. So MC design method has fixed in the design step to some degree. In Design and Work Execution of House the exclusive area of the apartment was calculated by face to face dimension. And the term of face to face dimension was known broadly to clients, construction company, etc. But design method of face to face dimension is not to simply extend the room size so far as wall depth in design process but to ensure the actual space should be considered with efficient use and assembly of building components. That is to say, Design method of face to face dimension is not to be defined by design step but to be related with construction and maintenance. However in construction process the point of face to face design method was not understood even now. So the purpose of this study was to extract the effect and question of face to face design method in construction process by survey of actual condition of structure and construction quality, and compare this result with existing construction method. The following project of this study is to survey of actual condition of interior components such as gypsum board, windows & doors etc. Therefore this study is to induce architectural long-life through architectural standardization construction and component's exchange, and, by the subject of the study on Apartment to have design guideline and basis for joining part between Gypsumboard and windows.

• Journal of Korea Multimedia Society
• /
• v.9 no.11
• /
• pp.1435-1444
• /
• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.