• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.552-555
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• 2018

This study developed a gesture recognition method using Kinect sensor and proposed a fighting action control interface. To extract the pattern features of a gesture, it used a method of extracting them in consideration of a body rate based on the shoulders, rather than of absolute positions. Although the same gesture is made, the positional coordinates of each joint caught by Kinect sensor can be different depending on a length and direction of the arm. Therefore, this study applied principal component analysis in order for gesture modeling and analysis. The method helps to reduce the effects of data errors and bring about dimensional contraction effect. In addition, this study proposed a modified matching algorithm to reduce motion restrictions of gesture recognition system.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.849-853
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• 2001

본 논문에서는 연속적인 인간의 제스처 영상을 저차원 제스처 특징 공간과 HMM 이용하여 인식할 수 있는 방법에 대해 소개한다. 일반적으로 제스처 공간에서 모델 패턴들과 매칭하기 위해서는 모든 모델 영상과 연속적인 입력영상들간의 거리평가로 인식을 수행하게 된다. 여기서 제안한 방법은 연속성을 가진 모델영상들을 HMM로 포즈들의 시공간적 특성을 매칭에 이용하였다. 이 방법은 동작의 구분뿐만 인식결과를 학습에 이용할 수 있는 장점이 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1480-1487
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• 2004

This study is proposed the novel gesture recognition method for the remote camera robot control. To recognize the dynamics gesture, the preprocessing step is the image segmentation. The conventional methods for the effectively object segmentation has need a lot of the cole. information about the object(hand) image. And these methods in the recognition step have need a lot of the features with the each object. To improve the problems of the conventional methods, this study proposed the novel method to recognize the dynamic hand gesture such as the MMS(Max-Min Search) method to segment the object image, MSM(Mean Space Mapping) method and COG(Conte. Of Gravity) method to extract the features of image, and the structure of recognition MLPNN(Multi Layer Perceptron Neural Network) to recognize the dynamic gestures. In the results of experiment, the recognition rate of the proposed method appeared more than 90[%], and this result is shown that is available by HCI(Human Computer Interface) device for .emote robot control.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.125-134
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• 2023

The purpose of this paper is to propose a method to improve the performance of the previous research is characterized by recognizing facial gestures from the 3D coordinates of seven landmarks selected from the MediaPipe Face Mesh model, generating corresponding user events, and executing corresponding commands. The proposed method applied adaptive moving average processing to the cursor positions in the process to stabilize the cursor by alleviating microtremor, and improved performance by blocking temporary opening/closing discrepancies between both eyes when opening and closing both eyes simultaneously. As a result of the usability evaluation of the proposed facial gesture interface, it was confirmed that the average recognition rate of facial gestures was increased to 98.7% compared to 95.8% in the previous research.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

This paper proposes a novel hidden Markov model (HMM)-based gesture recognition method and applies it to an HCI to control a computer game. The novelty of the proposed method is two-fold: 1) the proposed method uses a continuous streaming of human motion as the input to the HMM instead of isolated data sequences or pre-segmented sequences of data and 2) the gesture segmentation and recognition are performed simultaneously. The proposed method consists of a single HMM composed of thirteen gesture-specific HMMs that independently recognize certain gestures. It takes a continuous stream of pose symbols as an input, where a pose is composed of coordinates that indicate the face, left hand, and right hand. Whenever a new input Pose arrives, the HMM continuously updates its state probabilities, then recognizes a gesture if the probability of a distinctive state exceeds a predefined threshold. To assess the validity of the proposed method, it was applied to a real game, Quake II, and the results demonstrated that the proposed HMM could provide very useful information to enhance the discrimination between different classes and reduce the computational cost.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.421-425
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• 2006

최근 들어서 휴머노이드 로봇을 비롯한 로봇에 대하여 관심이 증대되고 있다. 이에 따라, 외모를 닮은 로봇 뿐 만 아니라, 사람과 상호 작용을 할 수 있는 로봇 기술의 중요성이 부각되고 있다. 이러한 상호 작용을 위한 효율적이고, 가장 자연스러운 방법 중의 하나가 비전을 기반으로 한 제스처 인식이다. 제스처를 인식하는데 있어서 가장 중요한 것은 손의 모양과 움직임을 인식하는3차원 제스처 인식이다. 본 논문에서는 3차원 손 제스처를 인식하기 위하여3차원 손 모델 추정 방법과 명령형 제스처 인식 시스템을 소개하고, 수화, 지화 등으로의 확장성을 위한 프레임워크를 제안한다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.438-442
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• 1999

제스처 인식은 직관적일 뿐 아니라, 몇 가지의 기본 구성요소에 의하여 코드화(code)가 용이하여, 인간과 컴퓨터의 상호작용(HCI, Human-Computer Interaction)에 있어서 폭넓게 사용되고 있다. 본 논문에서는 손의 모양이나 크기와 같은 개인차 및 조명의 변화나 배율과 같은 입력환경의 영향을 최소화하여, 특별한 초기화 과정이나 모델의 준비과정 없이도 제스처를 인식할 수 있고, 적은 계산량으로 실시간 인식이 가능한 제스처 인식 시스템의 개발을 목표로 한다. 본 논문에서는 손에 부착하는 센서나 마커 없이, CCD 카메라에 의하여 입력된 컬러영상에서, 컬러정보 및 동작정보를 이용하여 손영역을 추출하고, 추출된 손의 경계선 정보를 이용하여 경계선-중심 거리 함수를 생성했다. 그리고, 손가락의 끝 부분에서는 경계선-중심 거리가 극대점을 이룬다는 원리를 이용하여 생성된 함수의 주파수를 분석하여 극대점을 구함으로써 각각의 손가락 끝 위치를 찾고, 손의 자세를 인식하여 제스처를 인식했다. 또한 본 논문에서 제안된 제스처 인식 방법은 PC상에서 구현되어 그 유용성과 실효성이 증명되었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.481-486
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• 2011

Recently, most presentations have been presented on the screen using the computer. This paper suggests that the computer can be controlled by the gesturing recognition, without the help of any person or tools. If we use only information in the form of images, we should have a high-resolution camera for capturing the images and a computer that can treat high-resolution images. However, this paper will present a solution whereby a low-resolution camera can be used at the stage. It uses the supersonic sensor to trace the presenter's location and a low-resolution camera for capturing the necessary limited and small area. The gesture is defined by the number of fingers and one's hand positions which are recognized by the Erosion / Dilation and Subtraction algorithm. The system this paper addresses has improved 13%, when comparing tests between the image-only data system and this paper's system. The gesture recognition tests have a 98% success rate.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1035-1045
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• 2004

In this paper, we describe a method that can recognize gestures and evaluate the degree of the gestures from sequential gesture images by using Gesture Feature Space. The previous popular methods based on HMM and neural network have difficulties in recognizing the degree of gesture even though it can classify gesture into some kinds. However, our proposed method can recognize not only posture but also the degree information of the gestures, such as speed and magnitude by calculating distance among the position vectors substituting input and model images in parametric eigenspace. This method which can be applied in various applications such as intelligent interface systems and surveillance systems is a simple and robust recognition algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.913-914
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• 2017

최근 가상현실 기기가 발전함에 따라 가상현실 콘텐츠 시장이 활발하게 성장하고 있다. 그래서 가상현실 콘텐츠 개발이 활발히 이루어지고 있다. 가상현실에서 사용가능한 기기가 발전하여 가상현실 콘텐츠에서 추가적인 기기를 사용가능하도록 가상현실 콘텐츠 수정해야 되는 문제점이 있다. 가상현실 콘텐츠 수정 없이 추가적인 기기를 사용가능한 제스처 입력 방법이 필요하다. 제안한 방법은 기기에 관계없이 가상현실 콘텐츠에서 사용하기 위한 제스처 입력 시스템 설계 방법을 제안한다. HTC VIVE으로 제스처 입력 시스템을 러닝 게임에 적용하여 검증하였다.