• 로봇학회논문지
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• 제12권3호
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.

• 한국통신학회논문지
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• 제30권6C호
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• pp.570-576
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• 2005

얼굴 인식 분야에서 포즈의 변화는 인식률을 저하시키는 가장 심각한 문제로 알려져 있다. 본 논문에서는 이러한 포즈가 변화된 얼굴 영상에 대한 인식률을 높이기 위한 전처리 단계로 정면이 아닌 얼굴 영상을 정면 얼굴 영상으로 변환시키는 방법을 제안한다. 제안한 방법은 PCA 계수를 선형 변환 시키는 변환 행렬을 사용되는데 이 변환 행렬은 PCA 계수 사이의 선형적인 관계를 이용하여 구한다. 제안된 방법은 PCA/LDA를 이용한 얼굴 인식 알고리즘으로 검증하였으며, 실험 결과 제안된 방법이 얼굴 인식률을 $20\%$ 정도 향상시킴을 알 수 있었다.

• 전기학회논문지
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• 제64권5호
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• pp.766-778
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• 2015

In this paper, we design a hybrid system for recognition and tracking realized with the aid of polynomial based RBFNNs pattern classifier and particle filter. The RBFNN classifier is built by learning the training data for diverse pose images. The optimized parameters of RBFNN classifier are obtained by Particle Swarm Optimization(PSO). Testing data for pose image is used as a face image obtained under real situation, where the face image is detected by AdaBoost algorithm. In order to improve the recognition performance for a detected image, pose estimation as preprocessing step is carried out before the face recognition step. PCA is used for pose estimation, the pose of detected image is assigned for the built pose by considering the featured difference between the previously built pose image and the newly detected image. The recognition of detected image is performed through polynomial based RBFNN pattern classifier, and if the detected image is equal to target for tracking, the target will be traced by particle filter in real time. Moreover, when tracking is failed by PF, Adaboost algorithm detects facial area again, and the procedures of both the pose estimation and the image recognition are repeated as mentioned above. Finally, experimental results are compared and analyzed by using Honda/UCSD data known as benchmark DB.

• 한국컴퓨터정보학회논문지
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• 제21권10호
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• pp.11-19
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• 2016

The size of a display is large, The form becoming various of that do not apply to previous methods of gaze tracking and if setup gaze-track-camera above display, can solve the problem of size or height of display. However, This method can not use of infrared illumination information of reflected cornea using previous methods. In this paper, Robust pupil detecting method for eye's occlusion, corner point of inner eye and center of pupil, and using the face pose information proposes a method for calculating the simply position of the gaze. In the proposed method, capture the frame for gaze tracking that according to position of person transform camera mode of wide or narrow angle. If detect the face exist in field of view(FOV) in wide mode of camera, transform narrow mode of camera calculating position of face. The frame captured in narrow mode of camera include gaze direction information of person in long distance. The method for calculating the gaze direction consist of face pose estimation and gaze direction calculating step. Face pose estimation is estimated by mapping between feature point of detected face and 3D model. To calculate gaze direction the first, perform ellipse detect using splitting from iris edge information of pupil and if occlusion of pupil, estimate position of pupil with deformable template. Then using center of pupil and corner point of inner eye, face pose information calculate gaze position at display. In the experiment, proposed gaze tracking algorithm in this paper solve the constraints that form of a display, to calculate effectively gaze direction of person in the long distance using single camera, demonstrate in experiments by distance.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2000-2003
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• 2003

This paper presents a three-dimensional (3D) head pose estimation algorithm for robust face recognition. Given a 3D input image, we automatically extract several important 3D facial feature points based on the facial geometry. To estimate 3D head pose accurately, we propose an Error Compensated-SVD (EC-SVD) algorithm. We estimate the initial 3D head pose of an input image using Singular Value Decomposition (SVD) method, and then perform a Pose refinement procedure in the normalized face space to compensate for the error for each axis. Experimental results show that the proposed method is capable of estimating pose accurately, therefore suitable for 3D face recognition.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2004년도 SMICS 2004 International Symposium on Maritime and Communication Sciences
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• pp.106-109
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.211-212
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• 2007

Pose-variation factors present a significant problem in 2D face recognition. To solve this problem, there are various approaches for a 3D face acquisition system which was able to generate multi-view images. However, this created another pose estimation problem in terms of normalizing the 3D face data. This paper presents a 3D head pose-normalization method using 2D and 3D interaction. The proposed method uses 2D information with the AAM(Active Appearance Model) and 3D information with a 3D normal vector. In order to verify the performance of the proposed method, we designed an experiment using 2.5D face recognition. Experimental results showed that the proposed method is robust against pose variation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1887-1890
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• 2003

This paper presents a three-dimensional (3D) head pose estimation algorithm using the stereo image. Given a pair of stereo image, we automatically extract several important facial feature points using the disparity map, the gabor filter and the canny edge detector. To detect the facial feature region , we propose a region dividing method using the disparity map. On the indoor head & shoulder stereo image, a face region has a larger disparity than a background. So we separate a face region from a background by a divergence of disparity. To estimate 3D head pose, we propose a 2D-3D Error Compensated-SVD (EC-SVD) algorithm. We estimate the 3D coordinates of the facial features using the correspondence of a stereo image. We can estimate the head pose of an input image using Error Compensated-SVD (EC-SVD) method. Experimental results show that the proposed method is capable of estimating pose accurately.

• 전자공학회논문지CI
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• 제47권4호
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• pp.25-33
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• 2010

본 논문에서는 단일 카메라를 이용하여 얼굴의 움직임 정보를 추정하고 3차원 모델을 합성하기 위한 기법을 제안한다. 먼저 단일 카메라 입력 영상에서 사용자의 얼굴 영역 특징 점 취득을 위한 4개의 하부 이미지를 획득한다. 획득된 4개의 하부 이미지를 템플릿으로 사용하여 사용자 얼굴 영역의 정보를 추출하며, 이들 4개의 특징 점을 사용하여 사용자 얼굴과 카메라 영상 평면 사이의 사영 관계를 계산한다. 취득된 카메라 행렬로부터 얼굴의 움직임 정보인 이동과 회전 성분을 추정할 수 있으며, 이를 기반으로 3차원 모델의 자세 정보를 설정한 다음 이를 사용자 얼굴에 가상의 객체를 합성하기 위한 정보로 이용한다. 다양한 실험을 통하여 사용자 얼굴의 움직임에 대한 정보 추출의 정확도를 검증하였다.