• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.61-70
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• 2023

We investigated technological trends in an electrocardiogram (ECG)-based biometric cryptosystem that uses physiological features of ECG signals to provide personally identifiable cryptographic key generation and authentication services. The following technical details of the cryptosystem were investigated and analyzed: preprocessing of ECG signals, extraction of personally identifiable features, generation of quantified encryption keys from ECG signals, reproduction of ECG encryption keys under time-varying noise, and new security applications based on ECG signals. The cryptosystem can be used as a security technology to protect users from hacking, information leakage, and malfunctioning attacks in wearable/implantable medical devices, wireless body area networks, and mobile healthcare services.

• Convergence Security Journal
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• v.11 no.4
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• pp.31-36
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• 2011

Enterprise which has a core technology or organization which manages a core information will be walking into a critical situation like a ruins when organization's confidential information is outflowed. In the past confidential information that was leaked to the off-line, recently the outflow made possible through a variety of equipment at any time via the network based on the ubiquitous communication environment. In this paper, we propose to authenticate and block all packets transmitted via the network at real-time in order to prevent confidentials outflow. Especially in or der to differentiate between users who attempt to disclose confidentials, we propose to insert user's biometric informaion transparently at per-packet basis, and also verify a performance by simulation.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.4
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• pp.238-246
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• 2011

A 3-5 GHz UWB radar chip in 0.13 ${\mu}m$ CMOS process is presented in this paper. The UWB radar transceiver for surveillance and biometric applications adopts the equivalent time sampling architecture and 4-channel time interleaved samplers to relax the impractical sampling frequency and enhance the overall scanning time. The RF front end (RFFE) includes the wideband LNA and 4-way RF power splitter, and the analog signal processing part consists of the high speed track & hold (T&H) / sample & hold (S&H) and integrator. The interleaved timing clocks are generated using a delay locked loop. The UWB transmitter employs the digitally synthesized topology. The measured NF of RFFE is 9.5 dB in 3-5 GHz. And DLL timing resolution is 50 ps. The measured spectrum of UWB transmitter shows the center frequency within 3-5 GHz satisfying the FCC spectrum mask. The power consumption of receiver and transmitter are 106.5 mW and 57 mW at 1.5 V supply, respectively.

• ETRI Journal
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• v.45 no.4
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• pp.603-614
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• 2023

Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross-reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real-time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.142-148
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• 2024

It is difficult to apply authentication methods of existing wired or wireless networks to Internet of Things (IoT) devices due to their poor environment, low capacity, and low-performance processor. In particular, there are many problems in applying methods such as blockchain to the IoT environment. In this paper, edge computing is used to serve as a server that authenticates disposable templates among biometric information in an IoT environment. In this environment, we propose a lightweight and strong authentication procedure using the IoT-edge computing (IoT-EC) system based on elliptic curve cryptographic (ECC) and evaluate its safety.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1226-1232
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• 2004

Biometric authentication is rising technology for the security market of the next generation. But most of biometric systems are developed using only one of various biological features. Recently, there is a vigorous research for the standardization of various biometric systems. In this paper, we propose a web-based authentication system using three other verifiers based on functional, parametric, and structural approaches for one biometrics of handwritten signature, which is conformable to a specification of BioAPI introduced by BioAPI Consortium for a standardization of biometric technology. This system is developed with a client-server structure, and clients and servers consist of three layers according to the BioAPI structure. The proposed neb-based multiple authentication system of one biometrics can be used to highly increase confidence degree of authentication without additional several biological measurements, although rejection rate is a little increased. That is, the false accept rate(FAR) decreases on the scale of about 1:40,000, although false reject rate(FRR) increases about 2.7 times in the case of combining above three signature verifiers. So the proposed approach can be used as an effective identification method on the internet of an open network. Also, it can be easily extended to a security system using multimodal biometrics.

• Information Systems Review
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• v.23 no.4
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• pp.1-22
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• 2021

In the newly encountered economy caused by the Corona virus Disease-19, remote transaction becomes a new normal that disrupt traditional economic order. In the middle of the disruption, mobile tech is placed and remote finance on mobile is highly noticed and considered as an infra-tech to support the new ecology, In mobile finance. remote payment is becoming the most common service and personal identification on it is critical to operate the new service. There are various means of remotely identifying a person. Recently the use of biometric information is increasing. In this study, a correlation analysis was conducted on factors that effects to biometrics usage and the intention to use in remote personal identification. Variables for critical factor in the remote identification were classified into 4 groups such as Performance expectancy, Effort expectancy, Social influence, and Security. The empirical analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was conducted. The relationship between variables and the intention to use is also analyzed. In the study, stepwise regression analysis was conducted four times in which variables were adjusted in individual stage. As a result, the analysis suggests that performance expectancy, effort expectancy, social influence, security have positive effects for intention to use. Additionally, effort expectancy and security have moderating effects to intention to use depends on biometric authentication experience. The analysis has shown positive effect of variables grouped on the intention to use them. It also suggests that the intention to use biometric authentication will quickly grow. This study is expected to make a contribution to utilize and improve the use of biometric information in mobile payment.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.317-326
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• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.311-317
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• 2015

Phenotypic characterization and body biometric in 13 traits (height at withers, body length, chest girth, paunch girth, ear length, tail length, length of tail up to switch, face length, face width, horn length, circumference of horn at base, distances between pin bone and hip bone) were recorded in 233 adult Gojri buffaloes from Punjab and Himachal Pradesh states of India. Traits were analysed by using varimax rotated principal component analysis (PCA) with Kaiser Normalization to explain body conformation. PCA revealed four components which explained about 70.9% of the total variation. First component described the general body conformation and explained 31.5% of total variation. It was represented by significant positive high loading of height at wither, body length, heart girth, face length and face width. The communality ranged from 0.83 (hip bone distance) to 0.45 (horn length) and unique factors ranged from 0.16 to 0.55 for all these 13 different biometric traits. Present study suggests that first principal component can be used in the evaluation and comparison of body conformation in buffaloes and thus provides an opportunity to distinguish between early and late maturing to adult, based on a small group of biometric traits to explain body conformation in adult buffaloes.

• Animal Bioscience
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• v.36 no.6
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• pp.980-989
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• 2023

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.