• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.2
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• pp.61-70
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• 1997

Human-hand gestures have been used a means of communication among people for a long time, being interpreted as streams of tokens for a language. The signed language is a method of communication for hearing impaired person. Articulated gestures and postures of hands and fingers are commonly used for the signed language. This paper presents a system which recognizes the korean sign language (KSL) and translates the recognition results into a normal korean text and sound. A pair of data-gloves are used a sthe sensing device for detecting motions of hands and fingers. In this paper, we propose a dynamic gesture recognition mehtod by employing a fuzzy feature analysis method for efficient classification of hand motions, and applying a fuzzy min-max neural network to on-line pattern recognition.

• ETRI Journal
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• v.37 no.4
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• pp.793-803
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• 2015

We propose a novel HMI UI/UX for an in-vehicle infotainment system. Our proposed HMI UI comprises multimodal interfaces that allow a driver to safely and intuitively manipulate an infotainment system while driving. Our analysis of a touchscreen interface-based HMI UI/UX reveals that a driver's use of such an interface while driving can cause the driver to be seriously distracted. Our proposed HMI UI/UX is a novel manipulation mechanism for a vehicle infotainment service. It consists of several interfaces that incorporate a variety of modalities, such as speech recognition, a manipulating device, and hand gesture recognition. In addition, we provide an HMI UI framework designed to be manipulated using a simple method based on four directions and one selection motion. Extensive quantitative and qualitative in-vehicle experiments demonstrate that the proposed HMI UI/UX is an efficient mechanism through which to manipulate an infotainment system while driving.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.68-76
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• 1997

This paper presents a system which recognizes dynamic hand gestures for virtual reality(VR). A dynamic hand gesture is a method of communication for a computer and human who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gestrue produced by two persons with their hands may not have the same numerical values which are obtained through electronic sensors. In this paper, we apply a fuzzy min-max neural network and feature analysis method using fuzzy logic for on-line pattern recognition.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1349-1354
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• 2017

In this paper, we propose a fast hand-gesture recognition algorithm for embedded system. Existing hand-gesture recognition algorithm has a difficulty to use in a low performance system such as embedded systems and mobile devices because of high computational complexity of contour tracing method that extracts all points of hand contour. Instead of using algorithms based on contour tracing, the proposed algorithm uses concentric-circle tracing method to estimate the abstracted contour of fingers, then classify hand-gestures by extracting features. The proposed algorithm has an average recognition rate of 95% and an average execution time of 1.29ms, which shows a maximum performance improvement of 44% compared with algorithm using the existing contour tracing method. It is confirmed that the algorithm can be used in a low performance system such as embedded systems and mobile devices.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.195-202
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• 2011

Objective: The aim of this study is to investigate and review the previous researches about objective measuring fatigue caused by visual stimuli. Also, we analyze possibility of alternative visual fatigue measurement methods using facial expression recognition and gesture recognition. Background: In most previous researches, visual fatigue is commonly measured by survey or interview based subjective method. However, the subjective evaluation methods can be affected by individual feeling's variation or other kinds of stimuli. To solve these problems, signal and image processing based visual fatigue measurement methods have been widely researched. Method: To analyze the signal and image processing based methods, we categorized previous works into three groups such as bio-signal, brainwave, and eye image based methods. Also, the possibility of adopting facial expression or gesture recognition to measure visual fatigue is analyzed. Results: Bio-signal and brainwave based methods have problems because they can be degraded by not only visual stimuli but also the other kinds of external stimuli caused by other sense organs. In eye image based methods, using only single feature such as blink frequency or pupil size also has problem because the single feature can be easily degraded by other kinds of emotions. Conclusion: Multi-modal measurement method is required by fusing several features which are extracted from the bio-signal and image. Also, alternative method using facial expression or gesture recognition can be considered. Application: The objective visual fatigue measurement method can be applied into the fields of quantitative and comparative measurement of visual fatigue of next generation display devices in terms of human factor.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.419-425
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• 1999

In this paper, we present an algorithm of real time facial expression and gesture recognition for image sequence on the gray level. A mixture algorithm of a template matching and knowledge based geometrical consideration of a face were adapted to locate the face area in input image. And optical flow method applied on the area to recognize facial expressions. Also, we suggest hand area detection algorithm form a background image by analyzing entropy in an image. With modified hand area detection algorithm, it was possible to recognize hand gestures from it. As a results, the experiments showed that the suggested algorithm was good at recognizing one's facial expression and hand gesture by detecting a dominant motion area on images without getting any limits from the background image.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.768-775
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• 2005

For sign languages are main communication means among hearing-impaired people, there are communication difficulties between speaking-oriented people and sign-language-oriented people. Automated sign-language recognition may resolve these communication problems. In sign languages, finger spelling is used to spell names and words that are not listed in the dictionary. There have been research activities for gesture and posture recognition using glove-based devices. However, these devices are often expensive, cumbersome, and inadequate for recognizing elaborate finger spelling. Use of colored patches or gloves also cause uneasiness. In this paper, a vision-based finger spelling recognition system is introduced. In our method, captured hand region images were separated from the background using a skin detection algorithm assuming that there are no skin-colored objects in the background. Then, hand postures were recognized using a two-dimensional grid analysis method. Our recognition system is not sensitive to the size or the rotation of the input posture images. By optimizing the weights of the posture features using a genetic algorithm, our system achieved high accuracy that matches other systems using devices or colored gloves. We applied our posture recognition system for detecting Korean Sign Language, achieving better than $93\%$ accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3971-3975
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• 2013

This paper introduces an Augmented Reality system recognizing hand gestures and shows results of the evaluation. The system's user can interact with artificial objects and manipulate their position and motions simply by his hand gestures. Hand gesture recognition is based on Histograms of Oriented Gradients (HOG). Salient features of human hand appearance are detected by HOG blocks. Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification multiclass AdaBoostSVM algorithm is applied. Evaluated recognition rate of the algorithm is 94.0%.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.516-520
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• 2007

This paper presents a intuitive, simple and easy smart home TV remote control system using the hand gesture recognition. Hand candidate regions are detected by cascading policy of the part of human anatomy on the disparity map image, Exact hand region is extracted by the graph-cuts algorithm using the skin color information. Hand postures are represented by shape features which are extracted by a simple shape extraction method. We use the forward spotting accumulative HMMs for a smart home TV remote control system. Experimental results show that the proposed system has a good recognition rate of 97.33 % for TV remote control in real-time.