• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.945-952
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• 2007

The face detection is a necessary first-step in the face recognition systems, with the purpose of localizing and extracting face regions from input images. But it is not a simple problem, because faces have many variations such as scale, rotation and lighting condition. In this paper, we propose a novel method to detect not only frontal faces but also partial rotated faces in still images. Firstly, we produce the eye candidates in the sub-regions of an input image to detect rotated faces. Secondly, the eye candidates are used to measure the angles of rotated faces. Thirdly, we are able to derotate the rotated face then put it to Bayesian classifier. We make an experiment with rotated multiracial face and show the good results in this paper.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.85-92
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• 2021

In this paper, a method for effectively detecting rotated face and rotation angle regardless of the rotation angle is proposed. Rotated face detection is a challenging task, due to the large variation in facial appearance. In the proposed polar coordinate transformation, the spatial information of the facial components is maintained regardless of the rotation angle, so there is no variation in facial appearance due to rotation. Accordingly, features such as HOG, which are used for frontal face detection without rotation but have rotation-sensitive characteristics, can be effectively used in detecting rotated face. Only the training data in the frontal face is needed. The HOG feature obtained from the polar coordinate transformed images is learned using SVM and rotated faces are detected. Experiments on 3600 rotated face images show a rotation angle detection rate of 97.94%. Furthermore, the positions and rotation angles of the rotated faces are accurately detected from images with a background including multiple rotated faces.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.896-902
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• 2021

Rotated face detection is required in many applications but still remains as a challenging task, due to the large variations of face appearances. In this paper, a polar coordinate transform that is not affected by rotation is proposed. In addition, a method for effectively detecting rotated faces using the transformed image has been proposed. The proposed polar coordinate transform maintains spatial information between facial components such as eyes, mouth, etc., since the positions of facial components are always maintained regardless of rotation angle, thereby eliminating rotation effects. Polar coordinate transformed images are trained using AdaBoost, which is used for frontal face detection, and rotated faces are detected. We validate the detected faces using LBP that trained the non-face images. Experiments on 3600 face images obtained by rotating images in the BioID database show a rotating face detection rate of 96.17%. Furthermore, we accurately detected rotated faces in images with a background containing multiple rotated faces.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.31-33
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• 2009

Face detection using AdaBoost algorithm is capable of processing images rapidly while having high detection rates. It seemed to be the fastest and the most robust and it is still today. Many improvements or extensions of this method have been proposed. However, previous approaches only deal with upright faces. They suffer from limited discriminant capability for rotated faces as these methods apply the same features for both upright and rotated faces. To solve this problem, it is necessary that we rotate input images or make independently trained detectors. However, this can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. This paper proposes a robust algorithm for finding rotated faces within an image. It reduces the computational and sample complexity, by finding common features that can be shared across the classes. And it will be able to apply with multi-class object detection.

• 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 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.156.2-156.2
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• 2016

The variation of the surface structure of the Al adsorbed W(110) planes according to the coverage and the substrate temperature has been investigated using LEED and ISS When the Al atoms were adsorbed on the W(110) surface at room temperature, a p($1{\times}1$) of the fcc (111) face were found at the coverage higher than 4 ML. When the substrate temperature was kept at 900 K during Al adsorption and the coverage was 1.0 ML, the surface revealed a p($1{\times}1$) of the bcc(110) face and when the coverage is 1.5 ML, the surface showed a p($1{\times}1$) of the bcc (110) face together with a p($1{\times}1$) double domain structure (fcc (111) face) rotated ${\pm}3^{\circ}$ from the [100] direction of the W(110) surface. When Al atoms were adsorbed on the W(110) surface at the substrate temperature of 1000 K and the coverage was higher than 1.0 ML, the surface revealed a p($1{\times}1$) of the bcc(110) face together with p($1{\times}1$) double domain structure(fcc(111) face) rotated ${\pm}3^{\circ}{\sim}5^{\circ}$ from the [100] direction of the W(110) surface. When Al atoms were adsorbed on the W(110) surface at the substrate temperature of 1100 K and the coverage was 0.5 ML, Al atoms formed a p($2{\times}1$) double domain structure When the coverage was 1.0 ML, the double domain hexagonal structure (fcc(111) face) rotated ${\pm}5^{\circ}$ from the [100] direction of the W(110) surface and another distorted hexagonal structure was found. Low-energy electron diffraction results along with ion scattering spectroscopy results showed that the Al atoms followed the Volmer-Weber growth mode at high temperature.

• The Journal of Korean Physical Therapy
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• v.5 no.1
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• pp.17-24
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• 1993

The purpose of this study was to find out what changes in weight bearing in the both upper extremities occurred in response to Asymmetrical Tonic Neck Reflex by rotation of head in the quadruped position. The subjects for the study were 80 children (44 male, 44 female) who were neurologically normal. They were divided into two groups $6\~7$ year olds and $8\~9$ year olds. Paired t-test was applied as a atatistical method at the 0.01 level of significance. The results of this study are as follows; 1. In the neutral position of head, there was mort weight bearing on the left hand than the right in both of the two groups(p<0.01). 2. When the head was rotated to the right or left passively, there was more weight bearing on the hand of side to which the face was rotated(p<0.01). 3. When the head was rotated to the right or left actively, there us also more weight bearing on the hand of side to which the face was rotated(p<0.01). Therefore, it is possible to un Asymmetrical Tonic Neck Reflex to increase body weight for muscle strengthening in children with weakened muscles of the upper extremities. further studies are required for confirmation of these findings.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.987-988
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• 2008

In this paper, we propose a rotation invariant face detection method using Haar-like feature variation. Previous approaches using rectangular features can be calculated very fast. But rectangular features is weak in rotated face. Rotated Haar-like features can get high accuracy, but the performance is slow because it can't use the integral image. Our method vary Haar-like features keeping rectangular. this method makes the performance a bit slow, but gives better accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.984-994
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• 2010

Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.