• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.935-942
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• 2011

This study aims to design and implement the finger gesture recognizing system that automatically recognizes finger gestures input through a camera and controls the computer. Common CCD cameras were redesigned as infrared light cameras to acquire the images. The recorded images go through the pre-process to find the hand features, the finger gestures are read accordingly, and an event takes place for the follow-up mouse controlling and presentation, and finally the way to control computers is suggested. The finger gesture recognizing system presented in this study has been verified as the next-generation interface to replace the mouse and keyboard for the future information-based units.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.847-850
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• 2008

On today, human is going to develop machine by using mutual communication to machine. Including vision - based HCI(Human Computer Interaction), the technique which to recognize finger and to track finger is important in HCI systems, in HCI systems. In order to divide finger, this paper uses more effectively dividing the technique using subtraction which is separation of background and foreground, as well as to divide finger from limited background and cluttered background. In order to divide finger, the finger is recognized to make "Template-Matching" by identified fingertip images. And, identified gestures be compared the tracked gesture after tracking recognized finger. In this paper, after obtaining interest area, not only using subtraction image and template-matching but to perform template-matching in the area. So, emphasis is placed on decreasing perform speed and reaction speed, and we propose technique which is more effectively recognizing gestures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.91-97
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• 2013

A finger-gesture remote controller using the single camera is implemented in this paper, which is base on the recognition of finger number and finger moving direction. Proposed method uses the transformed YCbCr color-difference information to extract the hand region effectively. The number and position of finger are computer by using a double circle tracing method. Specially, a user continuous-command can be performed repeatedly by recognizing the finger-gesture direction of single shape. The position information of finger enables a user command to amplify a same command in the User eXperience. Also, all processing tasks are implemented by using the Intel OpenCV library and C++ language. In order to evaluate the performance of the our proposed method, after applying to the commercial video player software as a remote controller. As a result, the proposed method showed the average 89% recognition ratio by the user command-mode.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.117-123
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• 2016

A hand-gesture interface to manipulate a 3D object of augmented reality is implemented by recognizing the user hand-gesture in this paper. Proposed method extracts the hand region from real image, and creates augmented object by hand marker recognized user hand-gesture. Also, 3D object manipulation corresponding to user hand-gesture is performed by analyzing a hand region ratio, a numbet of finger and a variation ratio of hand region center. In order to evaluate the performance of the our proposed method, after making a 3D object by using the OpenGL library, all processing tasks are implemented by using the Intel OpenCV library and C++ language. As a result, the proposed method showed the average 90% recognition ratio by the user command-modes successfully.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.129-136
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• 2020

Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.29-37
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• 2016

Humans develop and maintain relationship through communication. Communication is largely divided into verbal communication and non-verbal communication. Verbal communication involves the use of a language or characters, while non-verbal communication utilizes body language. We use gestures with language together in conversations of everyday life. Gestures belong to non-verbal communication, and can be offered using a variety of shapes and movements to deliver an opinion. For this reason, gestures are spotlighted as a means of implementing an NUI/NUX in the fields of HCI and HRI. In this paper, using Kinect and the geometric features of the hand, we propose a method for recognizing the number of fingers and detecting the hand area. A Kinect depth image can be used to detect the hand region, with the finger number identified by comparing the distance of outline and the central point of a hand. Average recognition rate for recognizing the number of fingers is 98.5%, from the proposed method, The proposed method would help enhancing the functionality of the human computer interaction by increasing the expression range of gestures.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.91-96
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• 2015

We propose AirPincher, a handheld pointing device for recognizing delicate mid-air hand gestures to control a remote display. AirPincher is designed to overcome disadvantages of the two kinds of existing hand gesture-aware techniques such as glove-based and vision-based. The glove-based techniques cause cumbersomeness of wearing gloves every time and the vision-based techniques incur performance dependence on distance between a user and a remote display. AirPincher allows a user to hold the device in one hand and to generate several delicate finger gestures. The gestures are captured by several sensors proximately embedded into AirPincher. These features help AirPincher avoid the aforementioned disadvantages of the existing techniques. We experimentally find an efficient size of the virtual input space and evaluate two types of pointing interfaces with AirPincher for a remote display. Our experiments suggest appropriate configurations to use the proposed device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2363-2374
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• 2016

Communication occurs through verbal elements, which usually involve language, as well as non-verbal elements such as facial expressions, eye contact, and gestures. In particular, among these non-verbal elements, gestures are symbolic representations of physical, vocal, and emotional behaviors. This means that gestures can be signals toward a target or expressions of internal psychological processes, rather than simply movements of the body or hands. Moreover, gestures with such properties have been the focus of much research for a new interface in the NUI/NUX field. In this paper, we propose a method for recognizing the number of fingers and detecting the hand region based on the depth information and geometric features of the hand for application to an NUI/NUX. The hand region is detected by using depth information provided by the Kinect system, and the number of fingers is identified by comparing the distance between the contour and the center of the hand region. The contour is detected using the Suzuki85 algorithm, and the number of fingers is calculated by detecting the finger tips in a location at the maximum distance to compare the distances between three consecutive dots in the contour and the center point of the hand. The average recognition rate for the number of fingers is 98.6%, and the execution time is 0.065 ms for the algorithm used in the proposed method. Although this method is fast and its complexity is low, it shows a higher recognition rate and faster recognition speed than other methods. As an application example of the proposed method, this paper explains a Secret Door that recognizes a password by recognizing the number of fingers held up by a user.