• ETRI Journal
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• v.32 no.5
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.232-234
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• 2002

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present methods which jointly employ principal component analysis (PCA) and linear dynamical system (LDS) modeling for the task of EEG classification. Experimental study for the classification of EEG data during imagination of a left or right hand movement confirms the validity of our proposed methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.4
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• pp.179-183
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• 1984

Only K=$log_2$N reference patterns called principal components are sufficient to identify one out of N characters, calculated by computer and fablicated. An incogerent optical correlator is used for correlation coefficient measurements and it is shown that that this method has better discrimination ability that analog measuments using matched filter.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.133-139
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• 2008

Face recognition technologies using PCA(principal component analysis) recognize faces by deciding representative features of faces in the model image, extracting feature vectors from faces in a image and measuring the distance between them and face representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of face models of the same class is used as recognition unit for the images inputted on a continual input image. This paper proposes a new PCA recognition in which database of faces.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.2
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• pp.177-183
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• 2001

This paper proposes an efficient method for recognizing the fingerprint using the extracted features by adaptive principal component analysis(PCA). The adaptive PCA is implemented by a single-layer neural network for extracting the linear features of fingerprint data. And, the extracted data are transformed into binary data for reducing storage space and transmission time. The proposed method has been applied to recognize the 100 fingerprint data. The simulation results show that the recognitions are all successful and capable of about ${\pm}8^{\circ}$ rotated data.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.147-150
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• 2001

컴퓨터의 보편화와 멀티미디어의 발전으로 많은 인공지능의 분야들이 실생활에 응용되고 있다. 이 중에서 얼굴인식은 최근에 연구가 활발한 분야 중에 하나이며 다른 생체인식과는 달리 기계 장치에 신체의 일부를 접촉하지 않고 사람을 확인할 수 있다. 이러한 이유로 향후 생체인식 중 얼굴인식이 차지하는 비중은 커질 것으로 예상되고, 멀티미디어 보안 시스템 등에서 많은 응용이 기대되고 있다. 본 논문에서 정확한 사용자 인증을 위하여 기존의 주성분 분석(PCA; Principal Component Analysis)이 가지고 있는 단점인 조명에 영향을 많이 받는 것을 보완하기 위해, 다양한 조명에 안정적인 가보 함수를 같이 사용하였다. 주성분 분석만을 이용하는 것보다 사용자 인증의 성공률을 향상시킬 수 있음을 알 수 있었다.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.434-437
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• 2011

차세대 지능적 무기체계의 자동화를 목표로 SAR(Synthetic Aperture Radar) 영상 신호를 이용한 표적 인식률 향상을 위한 여러가지 방법들이 제안되어 왔다. 기존의 연구들은 SAR 영상의 고차원 특징을 그대로 사용했기 때문에 표적 인식의 성능저하가 있었다. 본 연구에서는 정보 획득 거리가 길고, 날씨에 제약이 없이 전천후 작전 운용이 가능하도록 레이더의 특징과 고해상도 영상을 결합한 SAR 이미지를 이용한 표적 인식률 향상 방법을 제안한다. 효과적인 표적 인식을 하기위해 고차원의 특징벡터를 저차원의 특징벡터로 축소하는 PCA(Principal Component Analysis)를 기반으로 하는 SVM(Support Vector Machine)을 사용한 표적 인식 기법을 사용하였고, PCA 기반의 SVM 분류기를 이용한 표적 인식이 SVM 만을 사용한 표적 인식보다 향상된 성능을 보인 것을 확인하였다.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.1
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• pp.33-38
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• 2002

This paper proposes an efficient method for improving the performance of polynomial adaline using the dimension reduction of independent variables. The adaptive principal component analysis is applied for reducing the dimension by extracting efficiently the features of the given independent variables. It can be solved the problems due to high dimensional input data in the polynomial adaline that the principal component analysis converts input data into set of statistically independent features. The proposed polynomial adaline has been applied to classify the patterns. The simulation results shows that the proposed polynomial adaline has better performances of the classification for test patterns, in comparison with those using the conventional polynomial adaline. Also, it is affected less by the scope of the smoothing factor.