• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.140-145
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• 2009

Significant attention has recently been drawn to human robot interaction system that uses face detection technology. The most conventional face detection methods have applied under pixel domain. These pixel based face detection methods require high computational power. Hence, the conventional methods do not satisfy the robot environment that requires robot to operate in a limited computing process and saving space. Also, compensating the variation of illumination is important and necessary for reliable face detection. In this paper, we propose the illumination invariant face detection that is performed under the compressed domain. The proposed method uses color balancing module to compensate illumination variation. Experiments show that the proposed face detection method can effectively increase the face detection rate under existing illumination.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.219-223
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• 2021

This study aims to present a non-face-to-face teaching methodology for the theory and practical lessons of design majors, especially for the arts and sports field. It was conducted to improve the existing teaching models after the non-face-to-face online lectures which began with COVID-19. Various existing smart learning methods such as online classes, interactive classes, and flip learning were reviewed, and a method to efficiently manage practical skills by supplementing the shortcomings of each study was suggested. 4 stages of teaching development - setting a teaching method, teaching progress, evaluation, and follow-up management-were designated and applied to the class of design majors. The result showed that it is effective in terms of teaching method and progress; however, the limitations of non-face-to-face classes were found in the stages of evaluation and follow-up management. Therefore, it is expected that further research on evaluation and follow-up management, such as specific practical instruction methods is required to improve completion.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.25-34
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• 2010

Robust face recognition under various illumination environments is difficult to achieve. For robust face recognition with respect to illumination variations, illumination normalization of face images is usually applied as a preprocessing step. Most of previously proposed illumination normalization methods cannot handle cast shadows in face images effectively. In this paper, We propose a new face illumination normalization method based on the illumination-separated face identity texture subspace. Since the face identity texture subspace is constructed so as to be separated from the effects of illumination variations, the projection of face images into the subspace produces a good illumination-normalized face images. Through experiments, it is shown that the proposed face illumination normalization method can effectively eliminate cast shadows as well as attached shadows and achieves a good face illumination normalization.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.615-618
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• 2002

This paper proposes a real-time face detection and tracking a method in complex backgrounds. The proposed method is based on the principal component analysis (PCA) technique. For the detection of a face, first, we use a skin color model and motion information. And then using the PCA technique the detected regions are verified to determine which region is indeed the face. The tracking of a face is based on the Euclidian distance in eigenspace between the previously tracked face and the newly detected faces. Camera control for the face tracking is done in such a way that the detected face region is kept on the center of the screen by controlling the pan/tilt platform. The proposed method is extensible to other systems such as teleconferencing system, intruder inspection system, and so on.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2720-2736
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• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.300-308
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• 2021

The epidemic of COVID19 caused a big change in design collaboration, which has been conducting using face-to-face interaction. Designers actively used video conferencing and online document tools in situations where face-to-face meetings were difficult, and this gave them an opportunity to experience that non-face-to-face collaboration can be more effective in a specific design process. In this regard, this study attempted to find out which activities during the design process are more compelling in face-to-face collaboration or non-face-to-face collaboration. To do this, three user experience design projects conducted after the COVID19 epidemic were analyzed through a retrospective interview method. As a result, during four design stages, 'Discover, Define, Develop, Deliver', face-to-face collaboration is necessary for the areas that require creative problem solving through active interaction. In contrast, non-face-to-face collaboration is preferred and more effective when designers need their own space and proceed their design work. In addition, the aspects of design tools supporting non-face-to-face collaboration were also illustrated. Findings discovered through this study are expected to contribute to research on the design process later.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1782-1787
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• 2003

A face recognition is currently the field which many research have been processed actively. But many problems must be solved the previous problem. First, We must recognize the face of the object taking a location various lighting change and change of the camera into account. In this paper, we proposed that new method to find feature within fast and correct computation time after scanning PC camera and ID card picture. It converted RGB color space to YUV. A face skin color extracts which equalize a histogram of Y ingredient without the Luminance. After, the method use V' ingredient which transforms V ingredient of YUV and then find the face feature. The result of the experiment shows getting correct input face image from ID Card picture and camera.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.987-990
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• 2005

An important issue in the field of face recognitions and man-machine interfaces is an automatic detection of faces in visual scenes. it should be computationally fast enough to allow an online detection. In this paper we describe our ongoing work on face detection that models the face appearance by edge orientation and color distribution. We show that edge orientation is a powerful feature to describe objects like faces. We present a method for face region detection using edge orientation and a method for face feature detection using local color information. We demonstrate the capability of our detection method on an image database of 1877 images taken from more than 700 people. The variations in head size, lighting and background are considerable, and all images are taken using low-end cameras. Experimental results show that the proposed scheme achieves 94% detection rate with a resonable amount of computation time.