• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.1
• /
• pp.9-16
• /
• 2005

In this paper, we compare face recognition rate by distance change using Principal Component Analysis algorithm being input left and right image in stereo image. Change to YCbCr color space from RGB color space in proposed method and face region does detection. Also, after acquire distance using stereo image extracted face image's extension and reduce do extract robust face region, experimented recognition rate by using PCA algorithm. Could get face recognition rate of 98.61%(30cm), 98.91%(50cm), 99.05%(100cm), 99.90%(120cm), 97.31%(150cm) and 96.71%(200cm) by average recognition result of acquired face image. Therefore, method that is proposed through an experiment showed that can get high recognition rate if apply scale up or reduction according to distance.

• Journal of Plant Biology
• /
• v.25 no.2
• /
• pp.83-99
• /
• 1982

In this study, thirty-two stands in Kwangnung forest located in the central part of Korea were preferentially selected. In each stand, all stems for trees and shrubs were recorded by species and their girths were measured down to 5cm. In addition, several enviromental factors such as field soil pH, field soil moisture, soil compressibility, depth of soil, thickness of litter layer, elevation and basal area were measured. Three soil cores were sampled and various physical and chemcial properties was determined. The vegetational data were subjected to three kinds of multivariate ordination(PO, PCA, RA). The results suggested that Kwangnung forest was consisted of three forest types: coniferous, mixed and broad leaved forest communities. The relation between the stand scores of ordination and several environmental factors were investigated in terms of correlation analysis in order to examine the relationships between the vegetation and certain environmental factors. As a result of this analysis, the amount of sand content in A1 horizon decreased frm the coniferous to broad leaved forest, while maximum field capacity, pore space, exchangeable cations, loss on ignition, soil pH nad the amount of total nitrogen had a tendancy to increase significantly. However, easily soluble phosphorus appeared to have little to do with the forest types. The result of species ordination of centered-standardized PCA suggested that the major successional pathway in Kwangnung forest was; Pinus densifloralongrightarrowQuercus mongolica, Q. serrata, Q. alienalongrightarrowCarpinus laxifloralongrightarrowC. erosa in sequence. This trend is in good agreement with the past studies. In three kinds of ordination (centered PCA, centered-standardized PCA and RA) based on nineteen species and twenty-five stands, the total variances accounted for the first three axes were 77%, 46% and 63% respectively. The estimated beta diversity in Kwangnung forest assumed as a coenocline, was 1.5~1.8 HC. Increasing the effect of the sampling errors on ordination perfermance, this low heterogeneity seems to cause the poor concentration of the total variance. The results from the four kinds of ordination were in good agreement with each other, especially between PO, centered-standardized PCA and RA appeared robust. It seems to be worthy of applying multivariate method for analyzing other forest communities in Korea.

• Journal of Internet Computing and Services
• /
• v.8 no.3
• /
• pp.19-32
• /
• 2007

This paper present a new face recognition method based on LVQ neural net to construct a real time face recognition system. The previous researches which used PCA, LDA combined neural net usually need much time in training neural net. The supervised LVQ neural net needs much less time in training and can maximize the separability between the classes. In this paper, the proposed method transforms the input face image by PCA and LDA sequentially into low-dimension feature vectors and recognizes the face through LVQ neural net. In order to make the system robust to external light variation, light compensation is performed on the detected face by max-min normalization method as preprocessing. PCA and LDA transformations are applied to the normalized face image to produce low-level feature vectors of the image. In order to determine the initial centers of LVQ and speed up the convergency of the LVQ neural net, the K-Means clustering algorithm is adopted. Subsequently, the class representative vectors can be produced by LVQ2 training using initial center vectors. The face recognition is achieved by using the euclidean distance measure between the center vector of classes and the feature vector of input image. From the experiments, we can prove that the proposed method is more effective in the recognition ratio for the cases of still images from ORL database and sequential images rather than using conventional PCA of a hybrid method with PCA and LDA.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.6
• /
• pp.183-190
• /
• 2016

This study proposed a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. The algorithm proposed detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). Also, by applying the feature vector obtained for SVM, face areas can be tested. After the testing, using the feature vector is final face recognition performed. The algorithm proposed in this study could increase the stability and accuracy of recognition rates and as a large amount of calculation was not necessary due to the use of two dimensions, real-time recognition was possible.

• The Journal of the Korea Contents Association
• /
• v.9 no.2
• /
• pp.115-124
• /
• 2009

The popular eigenfaces-based face recognition among proposed face recognition methods utilizes the eigenfaces obtained from applying PCA to a training face image set. Thus, it may not achieve a reliable performance under illumination environments different from that of training face images. In this paper, we propose an illumination-separate eigenfaces-based face recognition method, which excludes the effects of illumination as much as possible. The proposed method utilizes the illumination-separate eigenfaces which is obtained by orthogonal decomposition of the eigenface space of face model image set with respect to the constructed face illumination subspace. Through experiments, it is shown that the proposed face recognition method based on the illumination-separate eigenfaces performs more robustly under various illumination environments than the conventional eigenfaces-based face recognition method.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.450-453
• /
• 2006

Hydrographic data including particulate organic carbon (POC) from the Northeastern Gulf of Mexico (NEGOM) study were used along with remotely sensed data obtained from NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to develop POC algorithms to estimate POC concentration based on empirical and model-based principal component analysis (PCA) methods. In Case I and II waters empirical maximized simple ratio (MSR) and model-based PCA algorithms using full wavebands (blue, green and red wavelengths) provide more robust estimates of POC. The predicted POC concentrations matched well the spatial and seasonal distributions of POC measured in situ in the Gulf of Mexico. The ease in calculating the MSR algorithm compared to PCA analysis makes MSR the preferred algorithm for routine use. In order to determine the inter-annual variations of POC, MSR algorithms applied to calculate 100 monthly mean values of POC concentrations (September 1997-December 2005). The spatial and temporal variations of POC and sea surface temperature (SST) were analyzed with the empirical orthogonal function (EOF) method. POC estimates showed inter-annual variation in three different locations and may be affected by El $Ni{\tilde{n}}o/Southern$ Oscillation (ENSO) events.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.2
• /
• pp.111-119
• /
• 2013

Face recognition has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous applications, such as access control, surveillance, security, credit-card verification, and criminal identification. In this paper, we propose a simple descriptor called an ECSP(Extended Center-Symmetric Pattern) for illumination-robust face recognition. The ECSP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous CS-LBP(Center-Symmetric Local Binary Pattern). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. The facial texture information of the ECSP operator is then used as the input image of an image covariance-based feature extraction algorithm such as 2D-PCA(Two-Dimensional Principal Component Analysis). Performance evaluation of the proposed approach was carried out using various binary pattern operators and recognition algorithms on the Yale B database. The experimental results demonstrated that the proposed approach achieved better recognition accuracy than other approaches, and we confirmed that the proposed approach is effective against illumination variation.

• Proceedings of the KSPS conference
• /
• 2007.05a
• /
• pp.319-322
• /
• 2007

The goal of our research is to build a textindependent speaker identification system that can be used in mobile devices without any additional adaptation process. In this paper, we show that exploiting the advantages of both PCA(Principle Component Analysis) and LDA(Linear Discriminant Analysis) can increase the performance in the situation. The proposed method reduced the relative recognition error by 13.5%

• International journal of advanced smart convergence
• /
• v.7 no.4
• /
• pp.40-49
• /
• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• Journal of Korea Multimedia Society
• /
• v.13 no.11
• /
• pp.1630-1642
• /
• 2010

Recently, there is an increasing need for developing the technique of Hand Gesture Recognition (HGR), for vision based interface. Since hand gesture is defined as consecutive change of hand posture, developing the algorithm of Hand Posture Recognition (HPR) is required. Among the factors that decrease the performance of HPR, we focus on rotation factor. To achieve rotation invariant HPR, we propose a method that uses the property of video that adjacent frames in video have high correlation, considering the environment of HGR. The proposed method introduces template update of object tracking using the above mentioned property, which is different from previous works based on still images. To compare our proposed method with previous methods such as template matching, PCA and LBP, we performed experiments with video that has hand rotation. The accuracy rate of the proposed method is 22.7%, 14.5%, 10.7% and 4.3% higher than ordinary template matching, template matching using KL-Transform, PCA and LBP, respectively.