• 로봇학회논문지
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• 제1권1호
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• pp.66-72
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• 2006

In face recognition, learning speed of face is very important since the system should be trained again whenever the size of dataset increases. In existing methods, training time increases rapidly with the increase of data, which leads to the difficulty of training with a large dataset. To overcome this problem, we propose SVDD (Support Vector Domain Description)-based learning method that can learn a dataset of face rapidly and incrementally. In experimental results, we show that the training speed of the proposed method is much faster than those of other methods. Moreover, it is shown that our face recognition system can improve the accuracy gradually by learning faces incrementally at real environments with illumination changes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.102-104
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• 2004

Finger language is a typical tool for deaf persons. But learning the finger language for non-handicapped persons is very difficult. To overcome these difficulties, a new communication method using visual function is developed recently. Even though the developed system uses the visual function, it needs expensive equipments such as camera and computer. To be used in the real environments, the cost of equipments is a critical factor. If the recognition system for the finger language can be developed with low price equipments, the system can be used in the notebook or cellular phone. The image captured by PC camera was processed by preprocessing algorithm. To recognize the finger language, the resulting image was divide into $5{\times}5$ sections. The recognition system uses a similarity method and position information. The simulation results shows the effectiveness of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• 한국통신학회논문지
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• 제38A권2호
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• pp.174-182
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• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.

• 한국정보통신학회논문지
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• 제10권10호
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• pp.1920-1926
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• 2006

서비스 로봇의 사회 적 관심으로 인하여 서비스 로봇의 개발에 있어 많은 연구가 실시되고 있으나, 단일 플랫폼의 한계에 봉착해 있다. 이 한 한계를 극복하기 위하여 유비쿼터스 네트워크와 연계한 유비쿼터스 기반 서비스로 봇이 대안으로 자리 잡고 있다. 이를 위하여 유비쿼터스 센서 네트워크를 통하여 주변 환경에 대한 상황인식 및 위치 인식과 같은 기능을 위하여 RFID 및 초음파 센서를 이용한 시스템이 등장하여 실제 로봇에 적용되어 좋은 결과를 낳고 있다. 하지만 RFID의 경우 수동형 센서를 이용할 경우 거리에 따른 인식률의 제한이 따르며 초음파 센서의 경우 이를 구동하기 위하여 높은 전압을 요구하므로 저 전력 기반의 센서 네트워크에 응용하기에는 많은 한계가 따른다. 따라서 본 논문에서는 센서 네트워크 기반 위치인식을 위하여 센서 네트워크 모듈을 구현하고 이를 기반으로 RSSI 위치인식 시스템을 구현하고자 한다. 이러한 RSSI 위치인식 시스템의 경우 각 센서 노드에서 들어오는 신호의 RSSI만을 측정하고 이에 따른 거리로 환산하여 위치를 산출함으로 인하여 저 전력의 센서 네트워크를 그대로 활용할 수 있으며, Ad-Hoc 네트워크 설계시 거리에 따른 제한도 극복할 수 있을 것이다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권1호
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• pp.157-157
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• 1999

Researches on the gesture recognition have become a very interesting topic in the computer vision area, Gesture recognition from visual images has a number of potential applicationssuch as HCI (Human Computer Interaction), VR(Virtual Reality), machine vision. To overcome thetechnical barriers in visual processing, conventional approaches have employed cumbersome devicessuch as datagloves or color marked gloves. In this research, we capture gesture images without usingexternal devices and generate a gesture trajectery composed of point-tokens. The trajectory Is spottedusing phase-based velocity constraints and recognized using the discrete left-right HMM. Inputvectors to the HMM are obtained by using the LBG clustering algorithm on a polar-coordinate spacewhere point-tokens on the Cartesian space .are converted. A gesture vocabulary is composed oftwenty-two dynamic hand gestures for editing drawing elements. In our experiment, one hundred dataper gesture are collected from twenty persons, Fifty data are used for training and another fifty datafor recognition experiment. The recognition result shows about 95% recognition rate and also thepossibility that these results can be applied to several potential systems operated by gestures. Thedeveloped system is running in real time for editing basic graphic primitives in the hardwareenvironments of a Pentium-pro (200 MHz), a Matrox Meteor graphic board and a CCD camera, anda Window95 and Visual C++ software environment.

• 한국컴퓨터정보학회논문지
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• 제11권1호
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• pp.283-292
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• 2006

최근 보안관련 기술에 대한 관심이 높아지고 있으며, 보안 취약성을 극복하고자 노력들이 진행되고 있다. 온라인 사용자에 대한 인증도 생체 정보인 지문을 통한 방법들이 모색되고 있는 실정이다. 본 연구에서는 온라인 사용자에 대한 인증을 위한 지문인식 시스템으로 회전에 무관한 지문인식 시스템을 설계 구현하였다. 지문 이미지의 전처리 과정, 특징점 추출을 통한 정합 과정에 초점을 두었으며, 기존 연구에서 제시된 회전에 무관한 지문 인식시스템에서 처리시간과 인식률을 개선하였다. 또한 방향성 라플라시안 필터를 적용하여 기존 연구의 전처리 과정에서 발생하는 잡음, 왜곡 등의 문제들을 개선할 수 있었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권1호
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• pp.49-54
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• 2013

스마트폰 가속도 센서를 이용한 사용자 행위 인식은 동일한 행위를 수행하더라도 사용자마다 가속도 데이터 패턴이 서로 달라지는 사용자 의존성 문제를 가지고 있다. 그뿐만 아니라 스마트폰은 사용자의 어느 주머니나 손에도 놓일 수 있기 때문에 위치 의존성 문제도 지니고 있다. 본 논문에서는 특정 사용자나 특정 폰 위치에 대한 의존성이 적은 효과적인 행위 인식 방법을 제안한다. 제안한 방법을 기초로 안드로이드 스마트폰에서 동작하는 실시간 행위 인식 시스템을 구현하였다. 서로 다른 사용자와 서로 다른 폰 위치로부터 수집한 총 6642개의 샘플들을 이용한 실험을 통해, 본 논문에서 제안한 행위 인식 시스템의 성능을 분석하였다.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.121-126
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• 2022

The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.

• 로봇학회논문지
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• 제8권1호
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• pp.1-7
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• 2013

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.