• 대한전자공학회논문지SP
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• 제38권6호
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• pp.608-616
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• 2001

본 논문은 K-L 변환을 기반으로 한 Fisherface 알고리즘과 fixed graph matching (FGM) 방법을 이용하여 보다 효율적인 얼굴 인식 방법을 제안하고자 한다. 동적 링크 구조 방법 중에 하나인 elastic graph matching (EGM)은 얼굴의 모양 정보뿐만 아니라, 영상 픽셀의 그레이 정보를 동시에 이용하는 하며, 클래스를 구분하는 방법인 Fisherface 알고리즘은 빛의 방향 및 얼굴 표정과 같은 영상의 변화에 대해 강인하다고 알려져 있다. 위의 두 방법으로부터 제안한 알고리즘에서는 영상 그래프의 각 노드에 대해 Fisherface방법을 적용함으로써 레이블된 그래프 벡터의 차원을 줄일 뿐만 아니라 효율적으로 클래스를 구분하기 위한 특징 벡터를 제공한다. 그럼으로써 기존의 EGM 방법에 비해 인식 속도 면에서 상당한 향상 결과를 얻을 수 있었다. 특히, Olivetti Research Laboratory (ORL) 데이터베이스와 Yale 대학 데이터베이스에 대해 실험한 결과 제안한 얼굴 인식 알고리즘은 hold-out 방법에 의한 실험 결과, 평균 90.1%로 기존의 한 방법만을 사용한 것보다 높은 인식률을 보였다.

• 한국정보통신학회논문지
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• 제19권1호
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• pp.106-112
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• 2015

본 논문에서는 키넥트(Kinect)를 통해 얻은 깊이 영상에서 찾아낸 손가락의 끝점으로 임의의 3차원 공간인 공중에 그린 서명을 인식하는 알고리즘을 제안한다. 3차원 공간상에서 서명 궤적의 시프팅(Shifting), 스케일링(Scaling) 변화에 대응하기 위해 X, Y, Z좌표에 관한 각각 10개의 통계적 특징을 사용하였다. 인공신경망(Artificial Neural Network)은 기계학습 중 하나이며, 패턴인식 분야의 복잡한 분류 문제를 해결할 수 있는 도구로 사용되고 있다. 제안한 알고리즘을 실제 온라인 서명인식 시스템을 구현하여 적용하였고, 앞서 추출한 통계적 특징을 인공신경망의 입력값으로 사용하여 학습 과정을 거친 후 4가지 서명을 분류하는 것을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.281-286
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• 2010

We propose a space and time based sensor fusion method and a robust landmark detecting algorithm based on sensor fusion for mobile robot navigation. To fully utilize the information from the sensors, first, this paper proposes a new sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable an accurate measurement. Exploration of an unknown environment is an important task for the new generation of mobile robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. The newly proposed, STSF (Space and Time Sensor Fusion) scheme is applied to landmark recognition for mobile robot navigation in an unstructured environment as well as structured environment, and the experimental results demonstrate the performances of the landmark recognition.

• 한국광학회지
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• 제2권2호
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• pp.83-88
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• 1991

공간불변(space-invariant) 패턴인식에 대한 연구는 여러 접근방식에서 많은 시도가 이루어지고 있다. 학습 이미지의 가중치 선형조합(weighted linear summation)에 의한 SDF(synthetic discriminant function) 필터를 이용한 패턴인식은 그 중의 한 방식으로서 꾸준히 많은 관심을 받고 있다. 본 논문에서는 off-axis 평면기준파의 각분할(angular multiplexing) 방식과 pseude-inverse 알고리듬에 의한 pSDF 필터를 결합하여 상관기를 구성하고 상관면에서의 상관반응을 관측하여, off-axis pSDF 공간정합필터가 유형분류에 유용함을 입증하고, 광상관기로의 적용가능성을 보여주고자 한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.215-220
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• 2002

This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.

• 반도체디스플레이기술학회지
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• 제14권2호
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• 한국항공우주학회지
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• 제40권7호
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• pp.582-589
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• 2012

본 논문은 무인 항공기에서 지상의 차량을 촬영하여 차종을 인식하기 위한 알고리즘의 개발에 대해 논하고 있다. NCC(Normalized Cross-Correlation) 방법을 이용하여 영상에서 목표물의 기하학적인 정보를 정합하도록 하였고, 실제 비행영상을 통해 획득한 템플릿 이미지와 위성 지도를 통해 획득한 템플릿 이미지를 이용하여 영상의 정합을 수행하였다. 실내 기반 실험을 통해 정합 가능성을 평가하였으며, 위성 지도를 이용한 모의실험을 통해 NCC 알고리즘을 이용하여 차량의 종류를 식별할 수 있음을 확인하였다. 마지막으로 실제 비행 실험을 통해 획득한 영상을 통해 동일한 차량을 전체 영상에서 정합하는 실험을 수행하였다. 비행 실험 결과 승용차의 위치가 정확하게 탐지되었으며, 정합 결과 0.6점이상의 유사도가 나타남을 확인할 수 있었다. 또한 유사한 색상을 지닌 트럭은 정합하지 않음으로서 이종 차량의 구분이 가능함을 확인하였다.