• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1247-1259
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• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.330-336
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• 2011

In this paper, we propose a new method to improve the performance of supervector linear kernel SVM (Support Vector Machine) for speaker identification. This method is based on splitting one training datum into several pieces of utterances. We use four different databases for evaluating performance and use PCA (Principal Component Analysis), GKPCA (Greedy Kernel PCA) and KMDA (Kernel Multimodal Discriminant Analysis) for feature enhancement. As a result, the proposed method shows improved performance for speaker identification using supervector linear kernel SVM.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Journal of Korean Society of Transportation
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• v.27 no.1
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• pp.107-114
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• 2009

Human walking is essential and important mean of transportation. Pedestrian safety is recently important because accidents often happen while walking. This research is showing that Walking-environmental factors have effect on safety while walking. At first, exact 15 factors and conduct survey in the preceding research. After that, exact 4 important factors through factor analysis. At result of Multiple regression analysis, null hypothesis has proved to be true by satisfying therms which is F-value 9.211 and P-value 0.000. and come to the conclusion that walking-environmental factors influence pedestrian safety. 4 important factors can be listed by below. Pedestrian-road characteristic, landscape characteristic, commercial characteristic, walking characteristics by following influence. Especially, landscape characteristic and pedestrian-road characteristic can be vital factors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.5
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• pp.777-782
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using facial multi-feature to improve the accuracy of recognition and to consider the convenience of user . Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A feature found out the facial parameter distance ratio. We've sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.403-409
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• 2007

Contourlet transform is an extention of the wavelet transform in two dimensions using the multiscale and directional fillet banks. The contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. In this paper, we propose a face recognition system based on fusion methods using contourlet transform and PCA. After decomposing a face image into directional subband images by contourlet, features are obtained in each subband by PCA. Finally, face recognition is performed by fusion technique that effectively combines similarities calculated respectively In each local subband. To show the effectiveness of the proposed method, we performed experiments for ORL and CBNU dataset, and then we obtained better recognition performance in comparison with the results produced by conventional methods.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.936-941
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• 2006

To apply object recognition techniques to real environment, illumination compensation method should be developed. As effective illumination compensation model, we focused our attention on Retinex model and illumination-Reflectance model, implemented them, and experimented on their performance. We implemented Retinex model with Single Scale Retinex, Multi-Scale Retinex, and Retinex Neural Network and Multi-Scale Retinex Neural Network, neural network model of Retinex model. Also, we implemented illumination-Reflectance model with reflectance image calculation by calculating an illumination image by low frequency filtering in frequency domain of Discrete Cosine Transform and Wavelet Transform, and Gaussian blurring. We compare their illumination compensation performance to facial images under nine illumination directions. We also compare their performance after post processing using Principal Component Analysis(PCA). As a result, illumination Reflectance model showed better performance and their overall performance was improved when illumination compensated images were post processed by PCA.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.91-105
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• 1999

Spatial data integration using multiple geo-based data sets has been regarded as one of the primary GIS application issues. As for this issue, several integration schemes have been developed as the perspectives of mathematical geology or geo-mathematics. However, research-based approaches for statistical/quantitative assessments between integrated layer and input layers are not fully considered yet. Related to this niche point, in this study, spatial data integration using multiple geoscientific data sets by known integration algorithms was primarily performed. For spatial integration by using raster-based GIS functionality, geological, geochemical, geophysical data sets, DEM-driven data sets and remotely sensed imagery data sets from the Ogdong area were utilized for geological thematic mapping related by mineral potential mapping. In addition, statistical/quantitative information extraction with respective to relationships among used data sets and/or between each data set and integrated layer was carried out, with the scope of multiple data fusion and schematic statistical assessment methodology. As for the spatial integration scheme, certainty factor (CF) estimation and principal component analysis (PCA) were applied. However, this study was not aimed at direct comparison of both methodologies; whereas, for the statistical/quantitative assessment between integrated layer and input layers, some statistical methodologies based on contingency table were focused. Especially, for the bias reduction, jackknife technique was also applied in PCA-based spatial integration. Through the statistic analyses with respect to the integration information in this case study, new information for relationships of integrated layer and input layers was extracted. In addition, influence effects of input data sets with respect to integrated layer were assessed. This kind of approach provides a decision-making information in the viewpoint of GIS and is also exploratory data analysis in conjunction with GIS and geoscientific application, especially handing spatial integration or data fusion with complex variable data sets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.346-355
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using face, lips, to improve the accuracy of recognition. Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A voice recognition is found with MFCC by using mel frequency. We＇ve sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.