• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.359-362
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• 2016

Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

• The Journal of Society for e-Business Studies
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• v.21 no.4
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• pp.41-53
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• 2016

Today, leading smartphone manufacturers offer biometric technologies such as fingerprints, voice recognition, and iris patterns in their flagship models. These biometric technologies are used for authentication. Biometric authentications are widely used in device security and even in financial transaction. This paper examines cases where a user uses biometric authentication during financial transaction (both online and smartphone banking), and explains biometric for non-repudiation by digital signature. Finally, the paper also explains technical and legal requirements for biometric authentication in the area of financial services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.633-637
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• 2013

A wide range of applications for Foot Print Recognition System is discussed in this paper. The whole concept works under the principle that foot print is a parameter associated with biometrics that is very common as well as distinct. Its foremost application is at the government hospitals in the under developed and third world nations where there aren't the best of facilities. This system can be applied in the maternity ward of the hospitals for the identification or differentiation of the infants. Till date there has been no specialized system adopted for this purpose. The Foot Print Recognition System will overcome all the defects of any biometrics when applied here. Since the child will be very delicate for an iris scan and it will not be able to open its eyes wide or to correctly place its finger print on the sensor since the hands of a new born infant will be closed for a while. The Foot Print Recognition system can also be implemented in temples where there are cases of theft often reported. This can be used to grant access to the karpagraham of the deity by authorized users alone. These 2 applications of FPRS are discussed in this paper.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1114-1135
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• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2762-2777
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• 2017

A fuzzy identity based signature (FIBS) scheme allows a signer with identity ${\omega}$ to generate a signature which could be verified under identity ${\omega}^{\prime}$ if and only if ${\omega}$ and ${\omega}^{\prime}$ are within a certain distance of each other as judged by some metric. In this paper, we propose an efficient FIBS scheme from lattice assumption, which can resist quantum-computer attacks. Without using the Bonsai Tree technique, we utilize the lattice basis delegation technique to generate the private key, which has the advantage of keeping the lattice dimension invariant. We also prove that our proposed scheme is existentially unforgeable under an adaptive chosen message and identity attack in the random oracle model. Compared with existing scheme, our proposed scheme is much more efficient, especially in terms of communication overhead. Since our FIBS scheme possesses similar error-tolerance property, it can be well applied in post-quantum communication biometric authentication environments, where biometric identifiers such as fingerprints, voice, iris and gait are used in human identification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.824-827
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• 2005

The need to be able to identify other individual human beings is fundamental to the security and has been true since the beginning of human history. Physical or behavioral characteristics (finger-scan, face-scan, voice, palm, iris, retina, signature, human DNA, keystroke, vain, gait etc.) of a person are used to authenticate the person. The biometric technologies allow for a greater reliability of authentication as compared with password systems for physical access, network security, e-commerce, and so on. In this paper, we describe various technologies, market analysis, multi-modal system, standardization, privacy issue and future prospect of biometrics.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.653-656
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• 2019

현대 사회에서의 생체인식 기술의 발전을 통해 홍채, 지분 등의 정보를 사용하여 사용자의 신원확인 등이 가능하게 되었으며, 생체정보의 유출/위조 방지에 대한 중요성이 높아지면서 관련 연구가 활발히 이루어 지고 있다. 본 논문에서는 홍체 전체의 이미지에 삽입하거나 동공의 중심에 가까운 DCT(Discrete Cosine Transform)영역에 삽입방법, 동공 중심과 눈매의 영역을 검출하여 거리 및 DC 계수를 통하여 삽입 위치를 선택하는 방법 등 홍채영상 속 워터마크를 삽입하여 유출/위조된 홍채 영상을 검출하는 방법에서의 워터마크 삽입위치에 관한 방법을 연구하고, CASIA Irisimage Database ver 4.0의 워터마크를 삽입하여 NC(Normalized Correlation)의 값을 비교하여 워터마크 삽입 실험을 검증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.360-366
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• 2008

The initial fuzzy partitions in fuzzy rule-based classification systems are determined by considering the domain region of each attribute with the given data, and the optimal classification boundaries within the fuzzy partitions can be discovered by tuning their parameters using various learning processes such as neural network, genetic algorithm, and so on. In this paper, we propose a selection method for fuzzy partition based on statistical information to maximize the performance of pattern classification without learning processes where statistical information is used to extract the uncertainty regions (i.e., the regions which the classification boundaries in pattern classification problems are determined) in each input attribute from the numerical data. Moreover the methods for extracting the candidate rules which are associated with the partition intervals generated by statistical information and for minimizing the coupling problem between the candidate rules are additionally discussed. In order to show the effectiveness of the proposed method, we compared the classification accuracy of the proposed with those of conventional methods on the IRIS and New Thyroid Cancer data. From experimental results, we can confirm the fact that the proposed method only considering statistical information of the numerical patterns provides equal to or better classification accuracy than that of the conventional methods.

• Genomics & Informatics
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• v.11 no.2
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• pp.93-96
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• 2013

The prediction of externally visible characteristics from DNA has been studied for forensic genetics over the last few years. Externally visible characteristics include hair, skin, and eye color, height, and facial morphology, which have high heritability. Recent studies using genome-wide association analysis have identified genes and variations that correlate with human visible phenotypes and developed phenotype prediction programs. However, most prediction models were constructed and validated based on genotype and phenotype information on Europeans. Therefore, we need to validate prediction models in diverse ethnic populations. In this study, we selected potentially useful variations for forensic science that are associated with hair and eye color, iris pattern, and facial morphology, based on previous studies, and analyzed their frequencies in 1,920 Koreans. Among 20 single nucleotide polymorphisms (SNPs), 10 SNPs were polymorphic, 6 SNPs were very rare (minor allele frequency < 0.005), and 4 SNPs were monomorphic in the Korean population. Even though the usability of these SNPs should be verified by an association study in Koreans, this study provides 10 potential SNP markers for forensic science for externally visible characteristics in the Korean population.