• Journal of the Korea Society of Computer and Information
• /
• v.14 no.7
• /
• pp.9-15
• /
• 2009

The fuzzy weighted mean classifier is one of the most common classification models and could achieve high performance by adjusting the weights. However, the weights were generally decided based on the experience of experts, which made the resulting classifiers to suffer the lack of consistency and objectivity. To resolve this problem, in this paper, a weight deciding method based on the statistics of the data is introduced, which ensures the learned classifiers to be consistent and objective. To investigate the effectiveness of the proposed methods, Iris data set available from UCI machine learning repository is used and promising results are obtained.

• Journal of The Korean Astronomical Society
• /
• v.29 no.spc1
• /
• pp.287-289
• /
• 1996

IRIS(International Research on the Interior of the sun) is the name of a worldwide network of 6 stations for whole disc Doppler shift measurements. The network has been operating since 1987 and by now a few series of a hundred days long unbroken (by day/night periodicity) data were received. Analysis of these data allowed to receivesome new results which are discussed in the paper.

• The KIPS Transactions:PartB
• /
• v.10B no.4
• /
• pp.459-464
• /
• 2003

This paper proposes the use of a wavelet based image transform algorithm in human iris recognition method and the effectiveness of this technique will be determined in preprocessing of extracting Iris image from the user´s eye obtained by imaging device such as CCD Camera or due to torsional rotation of the eye, and it also resolves the problem caused by invariant under translations and dilations due to tilt of the head. This technique values through the proposed translation-invariant wavelet transform algorithm rather than the conventional wavelet transform method. Therefore we extracted the best-matching iris feature values and compared the stored feature codes with the incoming data to identify the user. As result of our experimentation, this technique demonstrate the significant advantage over verification when it compares with other general types of wavelet algorithm in the measure of FAR & FRR.

• Journal of Society of Preventive Korean Medicine
• /
• v.12 no.2
• /
• pp.131-143
• /
• 2008

This study aims at finding out the correlation between sasang constitution diagnosis using QSCCII which is self reporting questionnaire and sasang constitution diagnosis using iridology. In this study, 131 adult male and female participated as testees. The survey on questionnaires of QSCCII were carried out and then photos of irises of the participants were taken. The data from QSCCII was computed in WIN QSCCII version and they were translated into sasang constitution. For iris photos, the iris specialized camera with 1,300,000 picture elements was used, and then the iris photos were also read by computer. The correlation rate between QSCCII sasang constitution and iris sasang constitution was 52.4%, where kappa coefficient shows 0.323, which gives meaningful result statistically. And thus, the result can be used as a reference in sasang constitution diagnosis. For iridology to be served as a tool for sasang constitution diagnosis, criteria need to be developed for more concrete iris sasang constitution diagnosis in the future. And also comparative research is required among sasang constitution professionals and its iridological correlation of Sasang constitution carried out on more various group of people.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.705-708
• /
• 2018

Among biometrics commercialized for security, iris recognition technology has the most excellent security for the probability of the match between individuals is the lowest. Current commercialized iris recognition technology has excellent recognition ability, but this technology has a fatal drawback. Without the user's active cooperation, it cannot recognize the iris correctly. To make up for this weakness, recent trend of iris recognition development mounts a non-volunteering, unconstrained method. According to this information, the objective of this research is developing a module that can identify people iris from a video acquired by high performance infrared camera in a range of 3m and in a involuntary way. For this, we import images from the video and find people's face and eye positions from the images using Haar classifier trained through Cascade training method. finally, we crop the iris by Hough circle transform and compare it with data from the database to identify people.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.754-759
• /
• 2001

In this paper, we propose a TSK(Takagi-Sugeno-Kang)-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy c-Means) clustering, ANFIS(Adaptive Neuro-Fuzzy Inference System) and hybrid GA(Genetic Algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive GA) and RLSE(Recursive Least Square Estimate). Finally, we applied the proposed method to Iris data classificationl problems and obtained a better performance than previous works.

• Restorative Dentistry and Endodontics
• /
• v.29 no.2
• /
• pp.170-176
• /
• 2004

Objectives : In the unique metal iris method. the developing interfacial gap at the cavity floor resulting from the cavity wall property during polymerizing composite resin might affect the nominal shear bond strength values. The aim of this study is to evaluate that the iris method reduces the cohesive failure in the substrates and the cavity wall property effects on the shear bond strength tests using iris method. Materials and Methods : The occlusal dentin of 64 extracted human molars were randomly divided into 4 groups to simulate two different levels of cavity wall property (metal and dentin iris) and two different materials ($ONE-STEP^{\circledR}$ and $ALL-BOND^{\circledR}$ 2) for each wall property. After positioning the iris on the dentin surface. composite resin was packed and light-cured. After 24 hours the shear bond strength was measured at a crosshead speed of 0.5 mm/min. Fracture analysis was performed using a microscope and SEM. The data was analyzed statistically by a two-way ANOV A and t-test. Results : The shear bond strength with metal iris was significant higher than those with dentin iris (p＝0.034). Using $ONE-STEP^{\circledR}$, the shear bond strength with metal iris was significant higher than those with dentin iris (p＝0.005), but not in $ALL-BOND^{\circledR}$ 2 (p＝0.774). The incidence of cohesive failure was very lower than other shear bond strength tests that did not use iris method. Conclusions：The iris method may significantly reduce the cohesive failures in the substrates. According to the bonding agent systems. the shear bond strength was affected by the cavity wall property.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.3
• /
• pp.565-577
• /
• 2019

In recent years, biometric authentication has been used for various applications. Since biometric features are unchangeable and cannot be revoked unlike other personal information, there is increasing concern about leakage of biometric information. Recently, Jin et al. proposed a new cancelable biometric scheme, called "Index-of-Max" (IoM) to protect fingerprint template. The authors presented two realizations, namely, Gaussian random projection-based and uniformly random permutation-based hashing schemes. They also showed that their schemes can provide high accuracy, guarantee the security against recently presented privacy attacks, and satisfy some criteria of cancelable biometrics. However, the authors did not provide experimental results for other biometric features (e.g. finger-vein, iris). In this paper, we present the results of applying Jin et al.'s scheme to iris data. To do this, we propose a new method for processing iris data into a suitable form applicable to the Jin et al.'s scheme. Our experimental results show that it can guarantee favorable accuracy performance compared to the previous schemes. We also show that our scheme satisfies cancelable biometrics criteria and robustness to security and privacy attacks demonstrated in the Jin et al.'s work.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.2029-2032
• /
• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.5
• /
• pp.175-182
• /
• 2010

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.