• Journal of Internet Computing and Services
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• v.5 no.1
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• pp.59-66
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• 2004

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. in this paper, it proposed symmetric by using symmetry region searching and Wavelet Transform to reduce computation time of symmetry region searching, and It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction region.

• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1669-1678
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• 2002

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. In this paper, we proposed symmetric by using symmetry region searching in the reduced image to reduce computation time of symmetry region searching. It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction legion.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.53-59
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• 2011

In many face recognition applications such as security systems, it is assumed that upright faces are given to the system. In order for the system to be used in more general environments, the system should be able to deal with the rotated faces properly. It is a generally used approach to rotate the face detection window and apply face detector repeatedly to detect a rotated face in the given image. But such an approach requires a lot of computation time. In this paper, a method of extracting the axis of symmetry for a given set of points is proposed. The axis of symmetry for the edge points in the face detection window is extracted in a way that is fast and accurate, and the face detector is applied only for that direction. It is shown that the mean and standard deviation of the symmetry detection error is $0^{\circ}$ and $3^{\circ}$ respectively, for the database used.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.243-246
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• 2000

얼굴 영역 검출은 주어진 영상에서 얼굴의 유.무, 개수 및 위치를 검출하는 것으로 본 논문은 영상에서 얼굴을 검출하는 방법으로 신경망(Neural Network)을 적용하였다. 검출률의 향상 및 오검출률의 감소, 계산량을 최대한 줄이기 위해 후보 영역의 최적화와 얼굴의 대칭성(symmetry of face)을 이용한 좌우 평균 명암도 비교방법, 평균 얼굴 (average face)을 이용한 템플릿 매칭을 사용하였고, 실험을 통해서 제안한 방법이 효과적으로 수행됨을 보였다.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.21-28
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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 using a simple regression analysis combined with 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.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.5
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• pp.50-58
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• 1998

We propose an efficient face recognition system for controllinng the access to the restricted zone using both the face region detectors based on facial symmetry and the extended self-organizing maps (ESOM) which have sensory synapses and descriptive synapses. Based on the visual cues of the facial symmetry, we apply horizontal and vertical projections on elliptic regions detected by GHT(generalized hough transform) to identify all the face regions from the complex background.And we propose an ESOM which can exploit principal components and imitate an elastic similarity matching, to authenticate faces of the enlisted member. In order to cope with changes of facial experession or glasses wearing, etc, the facial descriptions of each member at the time of authentication are simultaneously updated on the discriptive synapses online using the incremental learning of the proposed ESOM. Experimental results prove the feasibility of our approach.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.9-15
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• 2016

Symmetry is everywhere in the world around us from galaxy to microbes. From ancient times symmetry is considered to be a reflection of the harmony of universe. Symmetry is not only a significant clue for human cognitive process, but also useful information for computer vision such as image understanding system. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, analysis of medical images, and so on. The technique used in this paper extracts edges, and the perpendicular bisector of any two edge points is considered to be a candidate axis of symmetry. The coefficients of candidate axis are accumulated in the coefficient space. Then the axis of symmetry is determined to be the line for which the coefficient histogram has maximum value. In this paper, an improved method is proposed that utilizes the directional information of edges, which is a byproduct of the edge detection process. Experiment on 20 test images shows that the proposed method performs 22.7 times faster than the original method. In another test on 5 images with 4% salt-and-pepper noise, the proposed method detects the symmetry successfully, while the original method fails. This result reveals that the proposed method enhances the speed and accuracy of detection process at the same time.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.115-118
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• 2001

An image processing method was applied to characterize a shape of the flexible grinding disk. A disk surface image was taken by CCD camera. Depth of cut was changed to be 2 and 4mm. Circles marked on the disk were captured to extract the key features of the deflection. Notable correlation has been observed between the intervals and the process conditions. Same methodology has been applied to check the symmetry of the human face. Tentative results revealed that symmetry could be checked using the filtered face image.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation