• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.27 no.3
• /
• pp.201-233
• /
• 2016

The purpose of this study is to improve awareness of cultural diversity and respect different culture, value and lifestyle by providing children from multi cultural family and children from non multi cultural family who are from third to sixth year in elementary school with education on cultural diversity. This study developed cultural diversity education program in public library and applied it to analyze a difference in awareness of cultural diversity education program, a difference in awareness of cultural diversity education program between experimental group and comparative group, library service, use of library service and influence of expansion of service. As a results, it was found that cultural diversity education program helped children understand, accept, consider and respect cultural diversity better.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.5
• /
• pp.63-69
• /
• 2004

This paper proposes a novel method for achieving pose-invariant face recognition using cylindrical model. On the assumption that a face is shaped like that of a cylinder, we estimate the object's pose and then extract the frontal face image via a pose transform with previously estimated pose angle. By employing the proposed pose transform technique we can increase the face recognition performance using the frontal face images. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the pose transform. Additionally, the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.5
• /
• pp.688-693
• /
• 2010

Vision based object recognition in mobile robots has many issues for image analysis problems with neighboring elements in dynamic environments. SURF(Speeded Up Robust Features) is the local feature extraction method of the image and its performance is constant even if disturbances, such as lighting, scale change and rotation, exist. However, it has a difficulty of real-time processing caused by representation of high dimensional vectors. To solve th problem, execution of SURF in GPU(Graphics Processing Unit) is proposed and implemented using CUDA of NVIDIA. Comparisons of recognition rates and processing time for SURF between CPU and GPU by variation of robot velocity and image sizes is experimented.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.6C
• /
• pp.570-576
• /
• 2005

Recognition of posed face is one of the most challenging problems in the field of face recognition. In this paper, as a preprocessing step for recognizing such faces, a method to transform non-frontal face images into frontal face images is proposed. The linear relationship between eigenfaces is utilized to obtain a pose transform matrix. The proposed method is verified with a well-known face recognition algorithm based on PCA/LDA. Compared to the conventional algorithm applied to the original posed face images, our experimental results indicated that the proposed method contributes to improve the recognition rate of such faces by $20\%$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.1137-1140
• /
• 2005

The work presented in this paper describes a Hidden Markov Model(HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the statics of the HMM are obtained using the coefficients of the Karhuman-Loves Transform(KLT). The face recognition method presented in this paper reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. In addition, the suggested method is more effective than the exiting ones in face extraction in terms of accuracy and others even under complex changes to the surroundings such as lighting.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.670-672
• /
• 2005

본 논문은 비전을 기반으로 한 감정인식 로봇에 관한 논문이다. 피부스킨칼라와 얼굴의 기하학적 정보를 이용한 얼굴검출과 감정인식 알고리즘을 제안하고, 개발한 로봇 시스템을 설명한다. 얼굴 검출은 RGB 칼라 공간을 CIElab칼라 공간으로 변환하여, 피부스킨 후보영역을 추출하고, Face Filter로 얼굴의 기하학적 상관관계를 통하여 얼굴을 검출한다. 기하학적인 특징을 이용하여 눈, 코, 입의 위치를 판별하여 표정 인식의 기본 데이터로 활용한다. 눈썹과 입의 영역에 감정 인식 윈도우를 적용하여, 윈도우 내에서의 픽셀값의 변화와 크기의 변화로 감정인식의 특징 칼을 추출한다. 추출된 값은 실험에 의해서 미리 구해진 샘플과 비교를 통해 강정을 표현하고, 표현된 감정은 Serial Communication을 통하여 로봇에 전달되고, 감정 데이터를 받은 얼굴에 장착되어 있는 모터를 통해 표정을 표현한다.

• Journal of KIISE
• /
• v.44 no.3
• /
• pp.295-305
• /
• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.

• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.369-374
• /
• 2002

In this paper, we propose the react-time car license plate recognition algorithm using intensity variation and geometric pattern vector. Generally, difference of car license plate region between character and background is more noticeable than other regions. And also, car license plate region usually shows high density values as well as constant intensity variations. Based on these characteristics, we first extract car license plate region using intensity variations. Secondly, lightness compensation process is performed on the considerably dark and brightness input images to acquire constant extraction efficiency. In the proposed recognition step, we first pre-process noise reduction and thinning steps. And also, we use geometric pattern vector to extract features which independent on the size, translation, and rotation of input values. In the experimental results, the proposed method shows better computation times than conventional circular pattern vector and better extraction results regardless of irregular environment lighting conditions as well as noise, size, and location of plate.

• Journal of KIISE
• /
• v.42 no.2
• /
• pp.203-212
• /
• 2015

This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.11C
• /
• pp.1140-1151
• /
• 2003

This paper proposes a face recognition method based on modified Otsu's binarization and Hu moment. Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. As the proposed modified Otsu's binarization computes other thresholds from conventional Otsu's binarization, namely we create two binary images, we can extract higher dimensional feature vector. Here the feature vector has properties of robustness to brightness and contrast changes because the proposed method is based on Otsu's binarization. And our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. In the perspective of brightness, contrast, scale, rotation, and translation changes, experimental results with Olivetti Research Laboratory (ORL) database and the AR database showed that average recognition rates of conventional well-known principal component analysis (PCA) are 93.2% and 81.4%, respectively. Meanwhile, the proposed method for the same databases has superior performance of the average recognition rates of 93.2% and 81.4%, respectively.