• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.473-478
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• 2003

In this paper, we propose the gesture recognition system using a motion information from extracted hand region in complex background image. First of all, we measure entropy for the difference image between continuous frames. Using a color information that is similar to a skin color in candidate region which has high value, we extract hand region only from background image. On the extracted hand region, we detect a contour using the chain code and recognize hand gesture by applying improved centroidal profile method. In the experimental results for 6 kinds of hand gesture, unlike existing methods, we can stably recognize hand gesture in complex background and illumination changes without marker. Also, it shows the recognition rate with more than 95% for person and 90∼100% for each gesture at 15 frames/second.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.79-87
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• 2018

The current PC user authentication uses character password based on user's knowledge. However, this can easily be exploited by password cracking or key-logging programs. In addition, the use of a difficult password and the periodic change of the password make it easy for the user to mistake exposing the password around the PC because it is difficult for the user to remember the password. In order to overcome this, we propose user gesture recognition and challenge-response authentication. We apply user's hand gesture instead of character password. In the challenge-response method, authentication is performed in the form of responding to a quiz, rather than using the same password every time. To apply the hand gesture to challenge-response authentication, the gesture is recognized and symbolized to be used in the quiz response. So we show that this method can be applied to PC user authentication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.616-619
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• 2004

As interests Un virtual reality being increased, the importance of HCI(Human computer interaction) field using gesture is also increased. However, in the preceding gesture recognition, the requirement of high-cost peripheral equipments limits users right. In this paper we suggest that through using low cost of USB PC-camera users are allowed to have more flexibly and cost down so that it can be adopted much commonly.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.113-120
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• 2017

With the development of virtual reality technology, in recent years, user-friendly hand gesture interface has been more studied for natural interaction with a virtual 3D object. Most earlier studies on the hand-gesture interface are using relatively simple hand gestures. In this paper, we suggest an intuitive hand gesture interface for interaction with 3D object in the virtual reality applications. For hand gesture recognition, first of all, we preprocess various hand data and classify the data through the binary decision tree. The classified data is re-sampled and converted to the chain-code, and then constructed to the hand feature data with the histograms of the chain code. Finally, the input gesture is recognized by MCSVM-based machine learning from the feature data. To test our proposed hand gesture interface we implemented a 'Virtual Block' game. Our experiments showed about 99.2% recognition ratio of 16 kinds of command gestures and more intuitive and user friendly than conventional mouse interface.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.396-402
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• 2022

User interactions are being developed in various forms, and in particular, interactions using human gestures are being actively studied. Among them, hand gesture recognition is used as a human interface in the field of realistic media based on the 3D Hand Model. The use of interfaces based on hand gesture recognition helps users access media media more easily and conveniently. User interaction using hand gesture recognition should be able to view images by applying fast and accurate hand gesture recognition technology without restrictions on the computer environment. This paper developed a fast and accurate user hand gesture recognition algorithm using the open source media pipe framework and machine learning's k-NN (K-Nearest Neighbor). In addition, in order to minimize the restriction of the computer environment, a stereoscopic image control system based on user hand gesture recognition was designed and implemented using a web service environment capable of Internet service and a docker container, a virtual environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.436-439
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• 2012

The work is to the control the desktop Computers based on hand gesture recognition. This paper is worked en real time tracking and recognizes the hand gesture for controlling the slides based on hand direction such as right and left using a real time camera.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.121-127
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• 2013

A vision based hand-gesture interface method for substituting a pointing device is proposed in this paper, which is used the ratio and size variation of Gesture Region. Proposed method uses the skin hue&saturation of the hand region from the HSI color model to extract the hand region effectively. This method can remove the non-hand region, and reduces the noise effect by the light source. Also, as the computation quantity is reduced by detecting not the static hand-shape recognition, but the ratio and size variation of hand-moving from the clipped hand region in real time, more response speed is guaranteed. In order to evaluate the performance of the our proposed method, after applying to the computerized self visual acuity testing system as a pointing device. As a result, the proposed method showed the average 86% gesture recognition ratio and 87% coordinate moving recognition ratio.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.