• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.483-486
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• 1999

In this paper, we present a real-time hand gesture recognition system that controls motion of a human avatar based on the pre-defined dynamic hand gesture commands in a virtual environment. Each motion of a human avatar consists of some elementary motions which are produced by solving inverse kinematics to target posture and interpolating joint angles for human-like motions. To overcome processing time of the recognition system for teaming, we use a Fuzzy Min-Max Neural Network (FMMNN) for classification of hand postures

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.66-74
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• 2018

Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1348-1353
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• 2007

We developed an augmented new reality tool for vision-based hand gesture recognition in a camera-projector system. Our recognition method uses modified Fourier descriptors for the classification of static hand gestures. Hand segmentation is based on a background subtraction method, which is improved to handle background changes. Most of the recognition methods are trained and tested by the same service-person, and training phase occurs only preceding the interaction. However, there are numerous situations when several untrained users would like to use gestures for the interaction. In our new practical approach the correction of faulty detected gestures is done during the recognition itself. Our main result is the quick on-line adaptation to the gestures of a new user to achieve user-independent gesture recognition.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.75-81
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• 2010

In this paper, we propose an emotion messenger which recognizes face or hand gestures of a user using a WebCAM, converts recognized emotions (joy, anger, grief, happiness) to flash-cones, and transmits them to the counterpart. This messenger consists of face recognition module, hand gesture recognition module, and messenger module. In the face recognition module, it converts each region of the eye and the mouth to a binary image and recognizes wink, kiss, and yawn according to shape change of the eye and the mouth. In hand gesture recognition module, it recognizes gawi-bawi-bo according to the number of fingers it has recognized. In messenger module, it converts wink, kiss, and yawn recognized by the face recognition module and gawi-bawi-bo recognized by the hand gesture recognition module to flash-cones and transmits them to the counterpart. Through simulation, we confirmed that CPU share ratio of the emotion messenger is minimized. Moreover, with respect to recognition ratio, we show that the hand gesture recognition module performs better than the face recognition module.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.41-48
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• 2018

The most natural way to increase immersion and provide free interaction in a virtual environment is to provide a gesture interface using the user's hand. However, most studies about hand gesture recognition require specialized sensors or equipment, or show low recognition rates. This paper proposes a three-dimensional DenseNet Convolutional Neural Network that enables recognition of hand gestures with no sensors or equipment other than an RGB camera for hand gesture input and introduces a virtual reality game based on it. Experimental results on 4 static hand gestures and 6 dynamic hand gestures showed that they could be used as real-time user interfaces for virtual reality games with an average recognition rate of 94.2% at 50ms. Results of this research can be used as a hand gesture interface not only for games but also for education, medicine, and shopping.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.6
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• pp.52-62
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• 1999

This paper presents a system which recognizes dynamic hand gestures on-line for controlling motion of numan avatar in virtual environment(VF). A dynamic hand gesture is a method of communication between a computer and a human being who uses gestures, especially both hands and fingers. A human avatar consists of 32 degree of freedom(DOF) for natural motion in VE and navigates by 8 pre-defined dynamic hand gestures. Inverse kinematics and dynamic kinematics are applied for real-time motion control of human avatar. In this paper, we apply a fuzzy min-max neural network and feature analysis method using fuzzy logic for on-line dynamic hand gesture recognition.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.