• 전자공학회논문지 IE
• /
• v.49 no.2
• /
• pp.82-88
• /
• 2012

In this paper, using the Android-based mobile platform designed and integrated U-healthcare systems for personal health care system is proposed. Integrated Biometric systems, electrocardiogram (ECG), oxygen saturation, blood pressure, respiration, body temperature, such as measuring vital signs throughout the module and signal processing biometric information through wireless communication module based on the Android mobile platform is transmitted to the gateway. Biometric data transmitted from a mobile health monitoring system, or transmitted to the server of U-healthcare was designed. By implementing vital signs monitoring system has been measured in vivo by monitoring data to determine current health status of caregivers had the advantage of being able to guarantee mobility respectively. This system is designed as personal health management and monitoring system for emergency patients will be helpful in the development looks U-healthcare system.

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

• The Journal of Korea Robotics Society
• /
• v.15 no.3
• /
• pp.286-292
• /
• 2020

This paper is about a fall inducement system for guiding like a real fall. Reliable fall data can be used as an essential element in developing effective fall protection devices. We can get this data if the induced fall is very realistic. The proposed system analyzes gait characteristics and determines when to fall based on the pedestrian's biometric data. To estimate the fall inducement time, an active estimation algorithm was proposed using different biometric values for each pedestrian. The proposed algorithm is designed to response actively to the ratio of gait cycle and a stance period. To verify this system, an experimental environment was implemented using a multi-rail treadmill equipped with a ground reaction force measurement device. An experiment was conducted to induce falls to pedestrians using a fall inducement system. By comparing the experimental scene to the video of the actual fall, it has been confirmed that the proposed system can induce a reliable fall.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.2
• /
• pp.43-50
• /
• 2018

Biometric information computing is greatly influencing both a computing system and Big-data system based on the bio-information system that combines bio-signal sensors and bio-information processing. Unlike conventional data formats such as text, images, and videos, biometric information is represented by text-based values that give meaning to a bio-signal, important event moments are stored in an image format, a complex data format such as a video format is constructed for data prediction and analysis through time series analysis. Such a complex data structure may be separately requested by text, image, video format depending on characteristics of data required by individual biometric information application services, or may request complex data formats simultaneously depending on the situation. Since previous bio-information processing computing systems depend on conventional computing component, computing structure, and data processing method, they have many inefficiencies in terms of data processing performance, transmission capability, storage efficiency, and system safety. In this study, we propose an improved biosensing converged big data computing architecture to build a platform that supports biometric information processing computing effectively. The proposed architecture effectively supports data storage and transmission efficiency, computing performance, and system stability. And, it can lay the foundation for system implementation and biometric information service optimization optimized for future biometric information computing.

• Proceedings of the IEEK Conference
• /
• 2000.11d
• /
• pp.247-250
• /
• 2000

This paper proposes a finger crease pattern identification algorithm utilizing a clustering method. The algorithms has been developed for the use of biometric person identification system. Since the finger crease pattern may be well-imaged utilizing low cost imaging devices such as low-end CCD camera with LED lighting, the feasibility of commercialization of the algorithm and the system utilizing the algorithm may be well justified if the finger crease pattern is a reasonable choice for the biometric feature. In this paper, we exploit this possibility and show the potential of using the finger crease pattern as a feature for biometric person identification.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.6
• /
• pp.395-402
• /
• 2016

In these days, the wearable devices have been developed for measuring biological data effectively. However, wearable devices have tissue allege and noise problem. Also, it is impossible for a remote center to identify the person whose data are measured by wearable devices, which could trigger a communication problem over treatment. To solve these problems, biometric measurement based on a non-contact method, such as face image sequencing is necessary. This makes it possible to measure biometric data without any operation and side effects. This system can monitor the biological signals of people in real time without allege and noise and simultaneously identify them. In this paper, we propose an authentication process while measuring biometric data, through a non-contact method.

• Journal of Korea Multimedia Society
• /
• v.6 no.7
• /
• pp.1139-1148
• /
• 2003

Biometric system has been studied as an optimal solution for preventing or reducing the peculation or loss of ID. Nowadays, face recognition has been spot-lighted as a future biometric system because it is not forced to contact the part of human body with the specific input area of the system. However, there is some limitations to get the constant facial features because the size of face area is varied by the capturing distance or tilt of the face. In this paper, we can extract constant facial features within the predefined threshold using the simple geometric processing such as image scaling, transformation, and rotation for frontal face images. This face recognition system identifies faces with 92% of accuracy for the 400 images of 40 different people.

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

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.415-420
• /
• 2013

U-WHS refers to a means of remote health monitoring service to combine fitness with wellbing. U-WHS is a system which can measure and manage biometric information of patients without any limitation on time and space. In this paper, we performed in order to look into the influence that the encryption module influences on the communication evaluation in the biometric information transmission gone to the smart mobile device and Hospital Information System.In the case of the U-WHS model, the client used the Objective-c programming language for software development of iOS Xcode environment and SEED and HIGHT encryption module was applied. In the case of HIS, the MySQL which is the Websocket API of the HTML5 and relational database management system for the client and inter-server communication was applied. Therefore, in WIFI communication environment, by using wireshark, data transfer rate of the biometric information, delay and loss rate was checked for the evaluation.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.1
• /
• pp.49-54
• /
• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.