• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1269-1278
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• 2002

The more high growth of knowledge, the more need personal identity technique. Fingerprint or iris of the eye identity techniques are already commercialized and used various field. Using human face recognition or authentication are not high performance yet. But application for an organism or face recognition are expected getting important. We propose a user recognition system by verifying similarity comparison of eye and lip component images which are splitted, calculated characteristic rate of each facial components and added weight to special formula. Through test proposed methods and analysis the result, we got a high recognition rate.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.305-314
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• 2017

Recently, user authentication through biometric traits such as fingerprint and iris raise more and more attention especially in mobile commerce and fin-tech fields. In particular, commercialized authentication methods using fingerprint recognition are widely utilized mainly because customers are more adopted and used to fingerprint recognition applications. In the meantime, the security issues caused by fingerprint falsification bring lots of attention. In this paper, we propose a new method to improve the performance of fake fingerprint detection using CNN(Convolutional Neural Network). It is common practice to increase the amount of learning data by using affine transformation or horizontal reflection to improve the detection rate in CNN characteristics that are influenced by learning data. However, in this paper we propose an effective data augmentation method based on the database difficulty level. The experimental results confirm the validity of proposed method.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.149-154
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• 2015

Biometric techniques for authentication using body parts such as a fingerprint, face, iris, voice, finger-vein and also photoplethysmography have become increasingly important in the personal security field, including door access control, finance security, electronic passport, and mobile device. Finger-vein images are now used to human identification, however, difficulties in recognizing finger-vein images are caused by capturing under various conditions, such as different temperatures and illumination, and noise in the acquisition camera. The human photoplethysmography is also important signal for human identification. In this paper To increase the recognition rate, we develop camera based identification method by combining finger vein image and photoplethysmography signal. We use a compact CMOS camera with a penetrating infrared LED light source to acquire images of finger vein and photoplethysmography signal. In addition, we suggest a simple pattern matching method to reduce the calculation time for embedded environments. The experimental results show that our simple system has good results in terms of speed and accuracy for personal identification compared to the result of only finger vein images.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.241-259
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• 2014

A PUF is a technology for distinguishing a device from other devices like biological information such as humans' iris or fingerprints. Over the past decade, many researchers studied various methods for implementing PUFs and utilizing them in identification, random number generation, key distribution and authentication. However, various attacks on the PUFs are the major reason to inhibiting the proliferation of PUF. For the reasons, various technologies are being studied to enhance safety of PUFs. In this paper, we will see several PUF implementations and various attacks on PUFs, and suggest guidelines for securely implementing PUFs. We expect our guidelines would be the foundation for implementing the secure and reliable PUFs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2124-2139
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• 2023

In this article, the authors focus on the use of smart CCTV, a combnation of biometric recognition technology and AI algorithms. In fact, the advancements in relevant technologies brought a significant increase in the use of biometric information - fingerprint, retina, iris or facial recognition - across diverse sectors. Both the public and private sectors, with the developments of biometric technology, widely adopt and use an individual's biometric information for different reasons. For instance, smartphone users highly count on biometric technolgies for the purpose of security. Public and private orgazanitions control an access to confidential information-controlling facilities with biometric technology. Biometric infomration is known to be unique and immutable in the course of one's life. Given the uniquness and immutability, it turned out to be as reliable means for the purpose of authentication and verification. However, the use of biometric information comes with cost, posing a privacy issue. Once it is leaked, there is little chance to recover damages resulting from unauthorized uses. The governments across the country fully understand the threat to privacy rights with the use of biometric information and AI. The EU and the United States amended their data protection laws to regulate it. South Korea aligned with them. Yet, the authors point out that Korean data aprotection law still requires more improvements to minimize a concern over privacy rights arising from the wide use of biometric information. In particular, the authors stress that it is necessary to amend Section (2) of Article 23 of PIPA to reflect the concern by changing the basis for permitting the processing of sensitive information from 'the Statutes' to 'the Acts'.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1007-1017
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• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.