• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.364-370
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• 2005

The first step of face recognition is to align an input face picture with database images. We propose a new algorithm of removing registration error in eigenspace. Our algorithm can correct for translation, rotation and scale changes. Linear matrix modeling of registration error enables us to compensate for subpixel errors in eigenspace. After calculating derivative of a weighting vector in eigenspace we can obtain the amount of translation or rotation without time consuming search. We verify that the correction enhances the recognition rate dramatically.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.283-294
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• 2006

In this paper, we propose a real-time face detection/tracking methodology with Haar wavelets and skin color. The proposed method boosts face detection and face tracking performance by combining skin color and Haar wavelets in an efficient way. The proposed method resolves the problem such as rotation and occlusion due to the characteristic of the condensation algorithm based on sampling despite it uses same features in both detection and tracking. In particular, it can be applied to a variety of applications such as face recognition and facial expression recognition which need an exact position and size of face since it not only keeps track of the position of a face, but also covers the size variation. Our test results show that our method performs well even in a complex background, a scene with varying face orientation and so on.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.267-274
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• 2018

This paper describes a head pose estimation method using PTZ(Pan-Tilt-Zoom) camera. When the external parameters of a camera is changed by rotation and translation, the estimated face pose for the same head also varies. In this paper, we propose a new method to estimate the head pose independently on varying the parameters of PTZ camera. The proposed method consists of 3 steps: face detection, feature extraction, and pose estimation. For each step, we respectively use MCT(Modified Census Transform) feature, the facial regression tree method, and the POSIT(Pose from Orthography and Scaling with ITeration) algorithm. The existing POSIT algorithm does not consider the rotation of a camera, but this paper improves the POSIT based on perspective projection in order to estimate the head pose robustly even when the external parameters of a camera are changed. Through experiments, we confirmed that RMSE(Root Mean Square Error) of the proposed method improve $0.6^{\circ}$ less then the conventional method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.183-190
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• 2016

This study proposed a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. The algorithm proposed detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). Also, by applying the feature vector obtained for SVM, face areas can be tested. After the testing, using the feature vector is final face recognition performed. The algorithm proposed in this study could increase the stability and accuracy of recognition rates and as a large amount of calculation was not necessary due to the use of two dimensions, real-time recognition was possible.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.675-678
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• 2018

Since the face has a unique property to identify human, the face recognition is actively used in a security area and an authentication area such as access control, criminal search, and CCTV. The frontal face image has the most face information. Therefore, it is necessary to acquire the front face image as much as possible for face recognition. In this study, the face region is detected using the Adaboost algorithm using Haar-like feature and tracks it using the mean-shifting algorithm. Then, the feature points of the facial elements such as the eyes and the mouth are extracted from the face region, and the ratio of the two eyes and degree of rotation of the face is calculated using their geographical information, and the approximate front face image is presented in real time.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2008-2011
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• 2003

얼굴인식의 전처리 단계는 주위의 배경으로부터 얼굴 영상을 분리하여 분석해야 한다. 이러한 전처리 단계는 환경적 요인으로 인해 많은 어려움을 가지고 있다. 또한, 개인별 특징의 차이, 얼굴의 기울어짐과 회전각도 및 영상내의 얼굴 크기 등으로 인해 어려움이 존재한다. 원영상을 입력받아 피부색을 통해 얼굴영역을 검출해 내어 사람의 표정변화에 가장 강인한 코 부분을 추출하여 워터쉐이드 변환을 하여 각 개인마다 다르게 가지고 있는 코의 패턴의 데이터를 저장하여 얼굴 인식에 이용할 수 있는 인자 값으로 이용한다. 따라서, 본 논문에서는 얼굴인식의 특징값을 코의 패턴을 이용하여 인식함으로써 다른 논문에서 제시하고 있는 눈의 특징이나 얼굴 각의 특징의 단점을 극복하여 보다 정화한 얼굴 인식을 할 수 있는 전처리 방법을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.750-753
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• 2012

A method detecting the position of eyes and tracking a gaze point of eyes in realtime using POSIT is suggested in this paper. This algorithm find out a candidate area of eyes using topological characteristics of eyes and then decides the center of eyes using physical characteristics of eyes. To find the eyes, a nose and a mouth are used for POSIT. The experimental results show that proposed method effectively performed detection of eyes in facial image in FERET databases and gave high performance when used for tracking a gaze point of eyes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.52-55
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• 2013

본 논문은 Boosted Random Ferns 기반의 회전 불변 얼굴 검출 방법을 제안한다. 기존 Random Ferns 의 경우 특징값을 추출할 때 임의로 선택한 두 픽셀의 밝기값 비교를 통하여 이진 특징값을 추출한다. 이 경우 해당 픽셀의 밝기값에 잡음이 포함되면 특징값이 부정확하게 추출되는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 임의로 두 블록을 선택하고 해당 블록내 밝기값의 평균을 비교하여 이진 특징값을 추출하였다. 또한 픽셀 위치를 임의로 선택하여 ferns 를 구성하였던 기존의 방법 대신 최고의 분류 성능을 가지는 fern 들을 이용하여 분류기를 구성하기 위해, AdaBoost 의 방법을 Random Ferns 에 맞게 변경하였다. Boosted Random Ferns 를 트리 구조의 cascade 노드에 방향과 각도에 따라 배치하여 연산 속도를 향상시키고 false-positive를 줄이는 효과를 보았다. CMU Rotated Face Database 를 사용하여 평가하였을 때, 기존 Random Ferns 는 false-positive 의 수가 57 개 일 때 66%의 검출률을 보인 반면, Boosted Random Ferns 는 false-positive 의 수가 45 개 일 때 88%의 검출률을 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.168-173
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• 2012

This paper presents a new human identification algorithm using partial features of the uncovered portion of face when a person wears a mask. After the face area is detected, the feature is extracted from the eye area above the mask. The identification process is performed by comparing the acquired one with the registered features. For extracting features SIFT(scale invariant feature transform) algorithm is used. The extracted features are independent of brightness and size- and rotation-invariant for the image. The experiment results show the effectiveness of the suggested algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.93-101
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• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.