• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1228-1233
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• 2019

Recently, a variety of algorithms using encryption technology have been adopted as methods of unlocking smartphone. It is advancing toward the direction to solve the unlocking problem through human biometrics technology, which has already succeeded in commercializing. These include finger print recognition, face recognition, and iris recognition. In this study, the evaluation items are five algorithms, including finger print recognition, face recognition, iris recognition, pattern recognition, and password input method. Based on the algorithms adopted, the AHP (analytic hierarchy process) technique was used to calculate the preferred priorities for smartphone users. Finger print recognition ( .400) was the top priority for smartphone users. Next, pattern recognition ( .237) was placed in the second priority for smartphone users. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.925-927
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• 2005

홍채 인식(Iris Recognition)은 동공과 흰자위 사이에 있는 홍채의 모양 패턴이 평생 변하지 않고, 사람마다 다른 패턴을 가진다는 특성을 이용하여 개인을 식별하는 기술로, 생체인식(Biometrics) 부분에서는 탁월한 식별력 및 신뢰성을 인정 받고 있다. 상당수의 기존 연구들은 원천 특허를 채택한 상태에서 성능 개선을 연구해 왔기 때문에 원천적인 한계를 가지고 있었다. 본 논문은 웨이블릿(Wavelet) 변환을 이용하여 특징을 추출하는 기존 방식과 다르게 프랙탈(Fractal) 방법으로 압축된 다수의 원 영상에 대해 입력된 영상의 유사도를 측정, 개인을 식별하는 새로운 홍채인식 방법을 제안한다. 이를 통해 타 연구들에서 제안했던 특별한 최적화 알고리즘을 사용하지 않고도 크게 떨어지지 않는 인식률을 얻을 수 있다.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.859-865
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• 2010

In this paper, a new method for finger vein recognition is proposed. Researchers are recently interested in the finger vein recognition since it is a good way to avoid the forgery in finger prints recognition and the inconveniences in obtaining images of the iris for iris recognition. The vein images are processed to obtain the line shaped vein images through the local histogram equalization and a thinning process. This thinned vein images are processed for matching, using a new matching algorithm, named HS(HeeSung) matching algorithm. This algorithm yields an excellent recognition rate when it is applied to the curve-linear images processed through a thinning or an edge detection. In our experiment with the finger vein images, the recognition rate has reached up to 99.20% using this algorithm applied to 650finger vein images(130person ${\times}$ 5images each). It takes only about 60 milliseconds to match one pair of images.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.251-258
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• 2003

A support vector machine (SVM) is supposed to provide a good generalization performance, but the actual performance of a actually implemented SVM is often far from the theoretically expected level. This is largely because the implementation is based on an approximated algorithm, due to the high complexity of time and space. To improve this limitation, we propose ensemble of SVMs by using Bagging (bootstrap aggregating) and Boosting. By a Bagging stage each individual SVM is trained independently using randomly chosen training samples via a bootstrap technique. By a Boosting stage an individual SVM is trained by choosing training samples according to their probability distribution. The probability distribution is updated by the error of independent classifiers, and the process is iterated. After the training stage, they are aggregated to make a collective decision in several ways, such ai majority voting, the LSE(least squares estimation) -based weighting, and double layer hierarchical combining. The simulation results for IRIS data classification, the hand-written digit recognition and Face detection show that the proposed SVM ensembles greatly outperforms a single SVM in terms of classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.1-11
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• 2008

In this paper, we propose the new eye detection method by using adaboost (adaptive boosting) method. Also, to reduce the false alarm rate which identifies the non-eye region as genuine eye that is the Problems of previous method using conventional adaboost, we proposed the post processing methods which used the cornea specular reflection and determined the optimized ratio of eye detecting box. Based on detected eye region by using adaboost, we performed the double circular edge detector for localizing a pupil and an iris region at the same time. Experimental results showed that the accuracy of eye detection was about 98% and the processing time was less than 1 second in mobile device.

• Journal of the HCI Society of Korea
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• v.11 no.1
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• pp.11-19
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• 2016

This study aimed to assess the economic value of the UX design and applied contingent valuation method (CVM) to the three smartphone screen unlock interface types: touch, fingerprint, and iris recognition. The contingent valuation method was chosen from various economic valuation approaches because the interface components such as the screen unlock are important user experience values but non-market goods which are not traded independently. Using the double-bounded dichotomous choice approach of the contingent valuation method, the survey results of the logit model showed that the economic value of touch unlock interface was 529 won, fingerprint was 4,214 won, and iris recognition was 1,316 won. That is, the fingerprint interface had the highest economic value, followed by iris recognition and touch interface. The main contribution of this research is that we examined a method for economic valuation of the UX design and generated systematic and credible results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.969-976
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• 2007

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation part using genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusion or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to two examples of the recognition of iris data and the recognition of Thyroid Gland cancer cells. The fuzzy classifier proposed in this paper has recognition rates of 98.67% for iris data and 98.25% for Thyroid Gland cancer cells.

• Journal of KIISE:Software and Applications
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• v.34 no.4
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• pp.377-388
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• 2007

In this paper, we propose a multi-modal biometric system based on face, iris and fingerprint recognition system. To effectively aggregate two systems, we use statistical distribution models based on matching values for genuine and impostor, respectively. And then, We performed reveal fusion algorithms including weighted summation, Support Vector Machine(SVM), Fisher discriminant analysis, Bayesian classifier. From the various experiments, we found that the performance of multi-modal biometric system was influenced with the normalization methods and classifiers.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.2
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• pp.23-29
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• 2002

In this paper we proposed the user identification system using hybrid biometric information and non-contact IC card to improve the accuracy of the system. The hybrid biometric information consists of the face image, the iris image, and the 4-digit voice password of user. And the non-contact IC card provides the base information of user If the distance between the sample hybrid biometric Information corresponding to the base information of user and the measured biometric information is less than the given threshold value, the identification is accepted. Otherwise it is rejected. Through the result of experimentation, this paper shows that the proposed method has better identification rate than the conventional identification method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.