• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.3
• /
• pp.1390-1403
• /
• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.9
• /
• pp.2309-2314
• /
• 1997

The study introduces a neural network to predict the bankruptcy of insurance companies. As a method to optimize the network, a genetic algorithm suggests optimal structure and network parameters. The neural network designed by genetic algorithm is compared with discriminant analysis, logistic regression, ID3, and CART. The robust neural network model shows the best performance among those models compared.

• Journal of Digital Convergence
• /
• v.11 no.12
• /
• pp.295-302
• /
• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.1
• /
• pp.1-15
• /
• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• ETRI Journal
• /
• v.33 no.3
• /
• pp.443-453
• /
• 2011

This paper evaluates the efficiency of mobile content firms through a hybrid approach combining data envelopment analysis (DEA) to analyze the relative efficiency and performance of firms and principal component analysis (PCA) to analyze data structures. We performed a DEA using the total amount of assets, operating costs, employees, and years in business as inputs, and revenue as output. We calculated fifteen combinations of DEA efficiency in the mobile content firms. We performed a PCA on the results of the fifteen DEA models, dividing the mobile content firms into those having either 'asset-oriented' or 'manpower and experience-oriented' efficiency. Discriminant analysis was used to validate the relationship between the efficiency models and mobile content types. This paper contributes toward the construction of a framework that combines the DEA and PCA approaches in mobile content firms for use in comprehensive measurements. Such a framework has the potential to present major factors of efficiency for sustainable management in mobile content firms and to aid in planning mobile content industry policies.

• Journal of Korean Society for Quality Management
• /
• v.24 no.4
• /
• pp.168-189
• /
• 1996

In order for the corporation to get a good image from the customers it should consider several variables, but especially important are corproate ethics and customer satisfaction through quality improvement. Standard multivariate data analysis can be applied to find out the importance of customer satisfaction and corporate ethics as influence factors in the corporate competitive strategy. When applying this Methodology, multivariate normal distributions density function and the identical covariance between groups assumptions have to be satisfied. By using the evaluation result from a small number of specialists in an attempt to decide on the strategical factors that will create a better company image than its competitor, if it chooses to use statistical discriminant analysis method, it would be difficult to satisfy the two assumptions mentioned above. This thesis introduces discriminant analysis method that uses LP/GP effectively which is applicable to this particular situation.

• KIEAE Journal
• /
• v.7 no.3
• /
• pp.11-20
• /
• 2007

The purpose of this study is to analyse the effects of the eco-friendly factors on the apartment housing price rise and to suggest the desirable way of marketing strategy for apartment housing. For the analysis, the data of apartment sites in Seoul had been collected from September 2006 to February 2007. The data consisted of 95 apartment sites in Seoul. Data were analyzed with descriptives, crosstabs, and discriminant analysis by SPSS/PC for Window. Following result was obtained. The eco-friendly apartment housing price rate in Seoul was determined by eco-friendly landscape, green space rate, house unit size, installment sale price per pyeong, floor space index, distance from subway station when it was not considered the impact of building age, construction company's brand, and autonomous districts. Findings of this research can provide valuable information for marketing strategy of housing construction company.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.4
• /
• pp.1269-1278
• /
• 2014

The urban highway, which is a motorway constructed to solve traffic issues, has the characteristic of extremely high damage to life during traffic accidents because the speed of vehicles is higher than typical roadways. In particular, because traffic accidents involving serious injuries hold a very important place among overall traffic accidents, analysis on factors affecting the occurrence of traffic accidents involving serious injuries must be considered with priority when establishing a reduction measure. Therefore, the study built a model that was capable of distinguishing the degree of the factors as part of microscopic analysis for investigating the complex effect of many elements concerning the occurrence of traffic accidents involving serious injuries in urban highways. The results are as follows. First, discriminant model showed a comparatively high level in overall accuracy rates, and, considering the correlation ratio, the models were determined to be valid, as all characteristics of the factors were clearly distinguished. Second, the problems of traffic accidents involving serious injuries on urban highways according to each factor, were clearly drawn out through the discriminant model. Third, the improvement measure for the problems drawn out from the discriminant models were clearly proposed.

• Economic and Environmental Geology
• /
• v.38 no.1
• /
• pp.45-55
• /
• 2005

Traditional GIS-based probabilistic spatial data integration models for landslide susceptibility analysis have failed to provide the theoretical backgrounds and effective methods for integration of different types of spatial data such as categorical and continuous data. This paper applies two spatial data integration models including non-parametric empirical estimation and parametric predictive discriminant analysis models that can directly use the original continuous data within a likelihood ratio framework. Similarity rates and a prediction rate curve are computed to quantitatively compare those two models. To illustrate the proposed models, two case studies from the Jangheung and Boeun areas were carried out and analyzed. As a result of the Jangheung case study, two models showed similar prediction capabilities. On the other hand, in the Boeun area, the parametric predictive discriminant analysis model showed the better prediction capability than that from the non-parametric empirical estimation model. In conclusion, the proposed models could effectively integrate the continuous data for landslide susceptibility analysis and more case studies should be carried out to support the results from the case studies, since each model has a distinctive feature in continuous data representation.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.867-876
• /
• 2017

Datasets with small n and large p are often found in various fields and the analysis of the datasets is still a challenge in statistics. Discriminant analysis models for such datasets were recently developed in classification problems. One approach of those models tries to detect dimensions that distinguish between groups well and the number of the detected dimensions is typically smaller than p. In such models, the number of dimensions is important because the prediction and visualization of data and can be usually determined by the K-fold cross-validation (CV). However, in sparse data scenarios, the CV is not reliable for determining the optimal number of dimensions since there can be only a few observations for each fold. Thus, we propose a method to determine the number of dimensions using a measure based on the standardized distance between the mean values of each group in the reduced dimensions. The proposed method is verified through simulations.