• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.701-709
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• 2019

Recently, heart rate signal, which is one of biological signals, have been used in various fields related to healthcare. Conventionally, most of the proposed heart rate signal detection methods are contact type methods, but there is a problem of discomfort that the subject have to contact with the device. In order to solve the problem, detection study by non-contact method has been progressed recently. The detected heart rate signal can be used for finger vein liveness detection and various application using heart rate. In this paper, we propose a method to obtain heart rate signal by using finger vein imaging system. The proposed method detected the signal from the changes of the brightness value in the time domain of the infrared finger vein images and converted it into the frequency domain using the image processing algorithm. After the conversion, we removed the noise not related to the heart rate signal through band-pass filtering. In order to evaluate the accuracy of the signal, we analyzed the correlation with the signal obtained simultaneously with the finger vein acquisition device and contact type PPG sensor approved by KFDA. As a result, it was possible to confirm that the heart rate signal detected in non-contact method through the finger vein image coincides with the waveform of actual heart rate signal.

• ETRI Journal
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• v.33 no.3
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• pp.415-422
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• 2011

In this paper, we present a novel vision-based method of recognizing finger actions for use in electronic appliance interfaces. Human skin is first detected by color and consecutive motion information. Then, fingertips are detected by a novel scale-invariant angle detection based on a variable k-cosine. Fingertip tracking is implemented by detected region-based tracking. By analyzing the contour of the tracked fingertip, fingertip parameters, such as position, thickness, and direction, are calculated. Finger actions, such as moving, clicking, and pointing, are recognized by analyzing these fingertip parameters. Experimental results show that the proposed angle detection can correctly detect fingertips, and that the recognized actions can be used for the interface with electronic appliances.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.159-168
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• 2021

As the information age enters, the use of biometrics using the body is gradually increasing because it is very important to accurately recognize and authenticate each individual's identity for information protection. Among them, finger vein authentication technology is receiving a lot of attention because it is difficult to forge and demodulate, so it has high security, high precision, and easy user acceptance. However, the accuracy may be degraded depending on the algorithm for identification or the surrounding light environment. In this paper, we designed and manufactured a side-type finger vein recognizer that is highly versatile among finger vein measuring devices, and authenticated using the deep learning model of DenseNet-201 for high accuracy and recognition rate. The performance of finger vein authentication technology according to the influence of the infrared light source used and the surrounding visible light was analyzed through simulation. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly were used, and the performance were compared and analyzed using the EER.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.23-30
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• 2021

Palmprint has become a popular biometric modality; however, palmprint recognition has not been conducted in video media. Video palmprint recognition (VPR) has some advantages that are absent in image palmprint recognition. In VPR, the registration and recognition can be automatically implemented without users' manual manipulation. A good-quality image can be selected from the video frames or generated from the fusion of multiple video frames. VPR in contactless mode overcomes several problems caused by contact mode; however, contactless mode, especially mobile mode, encounters with several revere challenges. Double-line-single-point (DLSP) assisted placement technique can overcome the challenges as well as effectively reduce the localization error and computation complexity. This paper modifies DLSP technique to reduce the invalid area in the frames. In addition, the valid frames, in which users place their hands correctly, are selected according to finger gap judgement, and then some key frames, which have good quality, are selected from the valid frames as the gallery samples that are matched with the query samples for authentication decision. The VPR algorithm is conducted on the system designed and developed on mobile device.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.4
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• pp.267-273
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• 2016

Hand posture recognition is an important technique to enable a natural and familiar interface in the human-computer interaction (HCI) field. This paper introduces a hand posture recognition method using a depth camera. Moreover, the hand posture recognition method is incorporated with the Moving Picture Experts Group Rich Media User Interface (MPEG-U) Advanced User Interaction (AUI) Interface (MPEG-U part 2), which can provide a natural interface on a variety of devices. The proposed method initially detects positions and lengths of all fingers opened, and then recognizes the hand posture from the pose of one or two hands, as well as the number of fingers folded when a user presents a gesture representing a pattern in the AUI data format specified in MPEG-U part 2. The AUI interface represents a user's hand posture in the compliant MPEG-U schema structure. Experimental results demonstrate the performance of the hand posture recognition system and verified that the AUI interface is compatible with the MPEG-U standard.

• Journal of the Optical Society of Korea
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• v.13 no.4
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• pp.445-450
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• 2009

Fingerprint recognition using a joint transform correlator (JTC) is the most well-known technology among optical fingerprint recognition methods. The JTC method optically compares the reference fingerprint image with the sample fingerprint image then examines match or non-match by acquiring a correlation peak. In contrast to the JTC method, this paper presents a new method to examine fingerprint recognition by producing a computer generated hologram (CGH) of those two fingerprint images and directly comparing them. As a result, we present some parameters to show that fingerprint recognition capability of the CGH direct comparison method is superior to that of the JTC method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.819-822
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• 2016

This paper proposes an algorithm recognizing finger using a distance graph of a detected finger's contour in a depth image. The distance graph shows angles and Euclidean distances between the center of palm and the hand contour as x and y axis respectively. We can obtain hand gestures from the graph using the fact that the graph has local maximum at the positions of finger tips. After we find the center of mass of the wrist using the fingers is thinner than the palm, we make its angle the orienting angle $0^{\circ}$. The simulation results show that the proposed algorithm can detect hand gestures well regardless of the hand direction.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.86-87
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• 2016

본 논문에서는 optical flow sensor를 이용하여 실내의 바닥 영상인식를 통한 영상처리기법을 이용해 움직임 없는 hovering을 할 수 있는 방법을 제안한다. 또한 optical flow와 BLE finger printing 기법을 혼합해 위치 인식 정밀도를 높일 수 있다. 본 고에서는 optical flow sensor와 BLE finger printing의 두 기술을 혼합하면 드론 스스로 실내에서 정밀도 높은 위치인식이 가능 하며 실외에서만 사용할 수 있는 GPS 비행모드를 대신 할 수 있어 실내에서 자동 경로 비행이 가능하게 하고 위치 안내, 실내 방송촬영, 이동식 CCTV등 질 높은 서비스를 제공하고자 한다.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.18-28
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• 2023

This study introduces a smart wrist band system with pressure measurements using wrist skin curvature variation due to finger motion. It is easy to wear and take off without pre-adaptation or surgery to use. By analyzing the depth variation of wrist skin curvature during each finger motion, we elaborated the most suitable location of each Force Sensitive Resistor (FSR) to be attached in the wristband with anatomical consideration. A 3D depth camera was used to investigate distinctive wrist locations, responsible for the anatomically de-coupled thumb, index, and middle finger, where the variations of wrist skin curvature appear independently. Then sensors within the wristband were attached correspondingly to measure the pressure change of those points and eventually the finger motion. The smart wrist band was validated for its practicality through two demonstrative applications, i.e., one for a real-time control of prosthetic robot hands and the other for natural human-computer interfacing. And hopefully other futuristic human-related applications would be benefited from the proposed smart wrist band system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.506-511
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• 2004

Biometrics is getting more and more attention in recent years for security and other concerns. So far, only fingerprint recognition has seen limited success for on-line security check, since other biometrics verification and identification systems require more complicated and expensive acquisition interfaces and recognition processes. Hand-Geometry has been used for biometric verification and identification because of its acquisition convenience and good performance for verification and identification performance. Hence, it can be a good candidate for online checks. Therefore, this paper proposes a Hand-Geometry recognition system based on geometrical features of hand. From anatomical point of view, human hand can be characterized by its length, width, thickness, geometrical composition, shapes of the palm, and shape and geometry of the fingers. This paper proposes thirty relevant features for a Hand-Geometry recognition system. However, during experimentation, it was discovered that length measured from the tip of the finger was not a reliable feature. Hence, we propose a new technique based on Genetic Algorithm for extraction of the center of nail bottom, in order to use it for the length feature.