• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.784-791
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• 2004

In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.608-616
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• 2001

This paper proposes a face recognition technique that effectively combines fixed graph matching (FGM) and Fisherface algorithm. EGM as one of dynamic link architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional EGM, the proposed approach could obtain satisfactory results in the perspectives of recognition speeds. Especially, we could get higher average recognition rate of 90.1% than the conventional methods by hold-out method for the experiments with the Yale Face Databases and Olivetti Research Laboratory (ORL) Databases.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2002.11a
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• pp.430-433
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• 2002

본 논문에서는 얼굴인식을 위해 기존의 Fisherfaces와 퍼지개념을 도입한 퍼지 기반 Fisherfaces 방법을 제안한다. 기존의 얼굴인식 방법들은 학습영상에 해당되는 각 특징벡터에 대해 특정한 클래스를 할당하지만, 이와는 달리 제안된 방법은 각 특징벡터에 대해 퍼지 값으로 된 클래스 소속도를 부여하여 조명의 방향, 얼굴표정과 같은 큰 변화에 민감하지 않으면서도 닮은 얼굴 영상으로 인해 생기는 오분류(misclassification)의 문제점을 해결하고자 한다. 따라서, 본 논문에서는 ORL(Olivetti Research Laboratory) 얼굴 데이터 베이스에 대해 적용하여 이전의 연구인 Eigenfaces와 Fisherfaces보다 더 좋은 인식성능을 보이고자 한다.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.94-98
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• 2009

In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.869-872
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• 2000

본 논문에서는 K-L 변환을 기반으로 한 Fisherface 알고리즘과 Elastic Graph Matching 방법을 사용하여 보다 효율적인 얼굴 인식 방법을 제안하고자 한다. 즉, 얼굴의 모양 정보뿐만 아니라, 영상 픽셀의 그레이 정보를 동시에 이용할 수 있는 Elastic Graph Matching 방법과 통계학적으로 신호의 차원을 줄일 뿐만 아니라 주위 환경의 변화에 강인한 Fisherface 알고리즘을 효율적으로 결합하고자 한다 본 연구에서는 얼굴을 인식하는 데에 있어서 주로 문제가 되는 광원의 위치에 따른 조도의 변화, 얼굴 표정 변화, 안경 착용 여부 등을 고려한 Yale대학 데이터베이스에 대해 기존의 여러 얼굴 인식알고리즘을 실험 및 비교하였다. 또한, 이들을 효율적으로 결합할 수 있는 인식 알고리즘을 제안함으로써 인식률 및 속도 면에서 비교적 만족할 만한 결과를 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.655-657
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• 2004

본 논문에서는 생체인식분야로 얼굴인식이 실제 시스템에서는 어느 정도의 인식률을 가질 수 있으며, 또한 얼굴 인식에서 많이 쓰이는 Fisherface에 Wavelet Transform으로 얼굴인식에서 단점인 대량의 데이터 핸들링을 극복하면서 인식률을 높일 수 있는 방법을 제안하였다. 실제로 원영상을 2차원 이산 웨이블릿 변환을 수행하면 4분의 1의 다운샘플링 된 저주파 영역만 뽑아 사용하므로 원영상을 이용한 Fisherface와 다름없는 높은 인식률을 보장하면서 데이터량을 줄여 얼굴인식의 데이터 부담을 줄일 수 있었다.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.231-234
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• 2001

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.364-367
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• 2003

In this study, we develop a method for recognizing face images by combining wavelet decomposition, fisherface method, and fuzzy integral. The proposed approach comprises of four main stages. The first stage uses the wavelet decomposition. As a result of this decomposition, we obtain four subimages. The second stage of the approach applies a fisherface method to these four subimage sets. The two last phases are concerned with the generation of the degree of fuzzy membership and the aggregation of the individual classifiers by means of the fuzzy integral. The experimental results obtained for the CNU and Yale face databases reveal that the approach presented in this study yields better classification performance in comparison to the results produced by other classifiers.

• Journal of Multimedia Information System
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• v.2 no.1
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• pp.171-178
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• 2015

This article describes 3 dimensional (3D) face recognition system using histogram of oriented gradients (HOG) based on face curvature. The surface curvatures in the face contain the most important personal feature information. In this paper, 3D face images are recognized by the face components: cheek, eyes, mouth, and nose. For the proposed approach, the first step uses the face curvatures which present the facial features for 3D face images, after normalization using the singular value decomposition (SVD). Fisherface method is then applied to each component curvature face. The reason for adapting the Fisherface method maintains the surface attribute for the face curvature, even though it can generate reduced image dimension. And histogram of oriented gradients (HOG) descriptor is one of the state-of-art methods which have been shown to significantly outperform the existing feature set for several objects detection and recognition. In the last step, the linear discriminant analysis is explained for each component. The experimental results showed that the proposed approach leads to higher detection accuracy rate than other methods.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.151-157
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• 2006

This paper is concerned with the template-based face recognition from robot camera images with illumination and distance variations. The approaches used in this paper consist of Eigenface, Fisherface, and Icaface which are the most representative recognition techniques frequently used in conjunction with face recognition. These approaches are based on a popular unsupervised and supervised statistical technique that supports finding useful image representations, respectively. Thus we focus on the performance comparison from robot camera images with unwanted variations. The comprehensive experiments are completed for a databases with illumination and distance variations.