• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.535-543
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• 2002

In this paper, we propose a new gaze detection method using 2-D facial images captured by a camera on top of the monitor. We consider only the facial rotation and translation and not the eyes' movements. The proposed method computes the gaze point caused by the facial rotation and the amount of the facial translation respectively, and by combining these two the final gaze point on a monitor screen can be obtained. We detected the gaze point caused by the facial rotation by using a neural network(a multi-layered perceptron) whose inputs are the 2-D geometric changes of the facial features' points and estimated the amount of the facial translation by image processing algorithms in real time. Experimental results show that the gaze point detection accuracy between the computed positions and the real ones is about 2.11 inches in RMS error when the distance between the user and a 19-inch monitor is about 50~70cm. The processing time is about 0.7 second with a Pentium PC(233MHz) and 320${\times}$240 pixel-size images.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.587-590
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• 1999

Gaze detection is to locate the position on a monitor screen where a user is looking at. We implement it by a computer vision system setting a camera above a monitor, and a user move (rotates and or translates) her face to gaze at a different position on the monitor. Up to now, we have tried several different approaches and among them the Two Neural Network approach shows the best result which is described in this paper (1.7 inch error for test data including facial rotation. 3.1 inch error for test data including facial rotation and translation).

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.835-838
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• 2008

In this paper, we present automatic face detection and tracking which is robustness in rotation and translation. Detecting a face image, we used Haar-like feature, which is fast detect facial image. Also tracking, we applied Lucas-Kanade feature tracker and KLT algorithm, which has robustness for rotated facial image. In experiment result, we confirmed that face detection and tracking which is robustness in rotation and translation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.212-214
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• 2021

In a society with Covid-19 as part of our daily lives, we had to adapt ourselves to a new reality to maintain our lifestyles as normal as possible. An example of this is teleworking and online classes. However, several issues appeared on the go as we started the new way of living. One of them is the doubt of knowing if real people are in front of the camera or if someone is paying attention during a lecture. Therefore, we encountered this issue by creating a 3D reconstruction tool to identify human faces and expressions actively. We use a web camera, a lightweight 3D face model, and use the 2D facial landmark to fit expression coefficients to drive the 3D model. With this Model, it is possible to represent our faces with an Avatar and fully control its bones with rotation and translation parameters. Therefore, in order to reconstruct facial expressions during online meetings, we proposed the above methods as our solution to solve the main issue.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.23-31
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• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.309-316
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• 2010

This paper introduces a new model based method for facial expression recognition that uses facial grid angles as feature space. In order to be able to recognize the six main facial expression, proposed method uses a grid approach and therefore it establishes a new feature space based on the angles that each gird's edge and vertex form. The way taken in the paper is robust against several affine transformations such as translation, rotation, and scaling which in other approaches are considered very harmful in the overall accuracy of a facial expression recognition algorithm. Also, this paper demonstrates the process that the feature space is created using angles and how a selection process of feature subset within this space is applied with Wrapper approach. Selected features are classified by SVM, 3-NN classifier and classification results are validated with two-tier cross validation. Proposed method shows 94% classification result and feature selection algorithm improves results by up to 10% over the full set of feature.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.23-35
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• 2002

Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a single camera above a monitor and a user moves (rotates and/or translates) his face to gaze at a different position on the monitor. To detect the gaze position, we locate facial region and facial features(both eyes, nostrils and lip corners) automatically in 2D camera images. From the movement of feature points detected in starting images, we can compute the initial 3D positions of those features by camera calibration and parameter estimation algorithm. Then, when a user moves(rotates and/or translates) his face in order to gaze at one position on a monitor, the moved 3D positions of those features can be computed from 3D rotation and translation estimation and affine transform. Finally, the gaze position on a monitor is computed from the normal vector of the plane determined by those moved 3D positions of features. As experimental results, we can obtain the gaze position on a monitor(19inches) and the gaze position accuracy between the computed positions and the real ones is about 2.01 inches of RMS error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.494-504
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• 2004

The researches about gaze detection have been much developed with many applications. Most previous researches only rely on image processing algorithm, so they take much processing time and have many constraints. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.2 cm of RMS error.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.372-378
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• 2003

Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.79-88
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• 2004

Gaze detection is to locate the position on a monitor screen where a user is looking by computer vision. Gaze detection systems have numerous fields of application. They are applicable to the man-machine interface for helping the handicapped to use computers and the view control in three dimensional simulation programs. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.