• 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 CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

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

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.196-204
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• 2008

In this paper, we propose an efficient method to classify human facial impression using frontal face image. The features that represent the shape of eye, jaw and face are used. The proposed method employs PCA, LDA and SVM in series. PCA is used to project the feature space to a low dimensional subspace. LDA produces well separated classes in a low dimensional subspace even under severe variation. This results in good discriminating power for classification. SVM is used to classify the data. Human face has been classified for 8 facial impressions. The experiments have been performed for many face images, and show encouraging result.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.371-376
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• 2003

In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.303-304
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• 2012

• Journal of the korean academy of Pediatric Dentistry
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• v.47 no.1
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• pp.17-24
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• 2020

The purpose of this study was to evaluate the accuracy and precision of the three-dimensional (3D) imaging system of children's facial soft tissue by comparing linear measurements. The subjects of the study were 15 children between the ages of 7 and 12. Twenty-three landmarks were pointed on the face of each subject and 16 linear measurements were directly obtained 2 times using an electronic caliper. Two sets of 3D facial images were made by the 3D scanner. The same 16 measurements were obtained on each 3D image. In the accuracy test, the total average difference was 0.9 mm. The precision of 3D photogrammetry was almost equivalent to that of direct measurement. Thus, 3D photogrammetry by the 3D scanner in children had sufficient accuracy and precision to be used in clinical setting. However, the 3D imaging system requires the subject's compliance for exact images. If the clinicians provide specific instructions to children while obtaining 3D images, the 3D device is useful for investigating children's facial growth and development. Also the device can be a valuable tool for evaluating the results of orthodontic and orthopedic treatments.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.31-38
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• 1998

본 논문에서는 애매성을 고려한 이론을 적용하여 유사도를 측정한 후 퍼지 관계 행 렬을 생성하여 인식을 행하는 방법을 제안하고자 한다. 인식 시스템은 모델과 입력 영상의 특징값을 정합하여 행하게 되는데 이때 얼마나 유사한가를 계산하는 유사도 측정은 대단히 중요한 작업중의 하나가 된다. 이를 위해 톨이론과 퍼지이론의 일치도 연산을 이용하여 유 사도를 측정하며, 퍼지 관계 행렬을 생성하여 정합을 행하고자 한다. 제안한 알고리즘에 대 해 3차원 물체와 얼굴 영상을 대상으로 실험을 수행하였으며 이를 통해 본 논문의 유용성을 입증하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.586-588
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• 2000

KUeyes는 3차원 실세계의 영상처리를 위해 고려대학교 인공시각연구센터에서 개발된 컬러 스테레오 헤드아이 시스템이다. KUeyes는 인간의 시각 시스템을 모델로 하여 다해상도 변환 영상, 칼라 정보와 거리 정보, 움직임 정보를 이용하여 지능적이고 빠르게 객체를 탐지하여 추적한다. 또한 병렬적으로 수행되는 인식기를 통해 탐지된 사람의 얼굴을 인식한다. 다양한 실험 및 분석을 통해 KUeyes가 복잡한 실영상을 대상으로 움직이는 개체를 신시간으로 안정되게 추적하고 인식하는 것을 확인할 수 있었다.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.321-326
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• 2004

Many researchers have been studied texturing the 3D face model with front and side pictures of ordinary person. It is very important to exactly detect the psition of eyes, nose, mouth of a human from the side pictures. Previous results first found the position of eye, nose, or mouth and then extract the other face components using their positional correlation. The detection results greatly depend on the correct extraction of the neck from the images. Therefore, we present a new algorithm that remove the neck completely and thus improve the detection rates of face components. To do this, we will use the RGB values and its differences.