• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.10
• /
• pp.129-136
• /
• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• Communications for Statistical Applications and Methods
• /
• v.11 no.3
• /
• pp.503-511
• /
• 2004

In Korea, OCR-based techniques have been developed for digital library construction of historical documents. In this paper, we propose the nonlinear transform of contour directional feature (CDF) vectors using log it and power transforms with skewness criterion to enhance the discriminant power. Experiments were conducted using samples from Seung-jung-won diaries (Diaries of King's Secretaries). Our results show that proposed method outperforms the others like Box-Cox transform in this database.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• pp.12-18
• /
• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.7
• /
• pp.608-614
• /
• 2014

A pattern recognition (PR) system is addressed for non-intrusive load monitoring. To effectively recognize two appliances (for example, an electric iron and a cook top), we propose a novel feature extraction method based on high order moments of power signals. Simulation results confirm that the PR system with the proposed high order moment features and kernel discriminant analysis can effectively separate two appliances.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1063-1064
• /
• 2007

본 연구는 유도전동기 진단시스템을 개발하기 위하여 테스트 전동기 내부에 취부된 자속센서 신호를 사용한 알고리즘 적용 결과를 논한 것으로서 분류기별 고장 판별 정확도에 대하여 서술하였다. 특징추출은 Kernel Principal Component Analysis (KPCA) 방법을 이용 하였으며, 테스트 샘플들에 대해서는 LDA(Linear Discriminant Analysis)와 k-NN(k-Nearest neighbors) 분류기법을 이용하여 판별하였다. 회전자 바 손상이나 편심(동적/정적)인 경우는 두 가지 분류기 모두 95[%]이상의 높은 분류 정확도를 보였지만, LDA인 경우 정상상태를 비롯한 베이링 불량이나, 샤프트 변형인 경우는 낮은 분류율을 보였다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.3
• /
• pp.254-259
• /
• 2015

In this paper, The wireless robot control system was proposed using Brain-computer interface(BCI) systems based on the steady-state visual evoked potential(SSVEP). Cross Power Spectral Density(CPSD) was used for analysis of electroencephalogram(EEG) and extraction of feature data. And Linear Discriminant Analysis(LDA) and Support Vector Machine(SVM) was used for patterns classification. We obtained the average classification rates of about 70% of each subject. Robot control was implemented using the results of classification of EEG and commanded using bluetooth communication for robot moving.

• ETRI Journal
• /
• v.35 no.6
• /
• pp.1105-1114
• /
• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.