• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)

• 반도체디스플레이기술학회지
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• 제7권3호
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• pp.45-49
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• 2008

Face detection and tracking technology on video sequence has developed indebted to commercialization of teleconference, telecommunication, front stage of surveillance system using face recognition, and video-phone applications. Complex background, color distortion by luminance effect and condition of luminance has hindered face recognition system. In this paper, we have proceeded to research of face recognition on video sequence. We extracted facial area using luminance and chrominance component on $YC_bC_r$ color space. After extracting facial area, we have developed the face recognition system applied to our improved algorithm that combined PCA and LDA. Our proposed algorithm has shown 92% recognition rate which is more accurate performance than previous methods that are applied to PCA, or combined PCA and LDA.

• ETRI Journal
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• 제33권2호
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• pp.251-258
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• 2011

Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home-service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real-AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually-marked eye position.

• 정보처리학회논문지B
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• 제9B권4호
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• pp.491-500
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• 2002

본 논문에서는 칼라 얼굴 영상으로부터 피부색 정보, 얼굴의 기하학적 특징벡터 및 안면각 정보를 이용한 실시간 얼굴검출 및 인식 알고리즘을 제안하였다. 제안한 알고리즘에서는 HSI 칼라좌표계상의 얼굴 피부색 정보와 얼굴 에지 정보를 함께 이용함으로써 얼굴 영역 검출 효율을 개선하였다. 또한 추출된 얼굴 영역으로부터 얼굴인식율 개선을 위해 얼굴 특징자들을 추출하고 추출된 얼굴 특징자들의 기하학적 관계로 구성된 얼굴 특징벡터와 얼굴 안면각 정보를 사용하여 얼굴 인식율을 개선하였다. 실험에서는 제안한 방법이 기존의 방법에 비해 얼굴 영역 검출율 뿐만 아니라 얼굴 인식율도 개선되었음을 알 수 있다.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.783-788
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• 2003

In this paper, we present a new approach to detect and recognize human face in the image from vision camera equipped on the mobile robot platform. Due to the mobility of camera platform, obtained facial image is small and pose-various. For this condition, new algorithm should cope with these constraints and can detect and recognize face in nearly real time. In detection step, ‘coarse to fine’ detection strategy is used. Firstly, region boundary including face is roughly located by dual ellipse templates of facial color and on this region, the locations of three main facial features- two eyes and mouth-are estimated. For this, simplified facial feature maps using characteristic chrominance are made out and candidate pixels are segmented as eye or mouth pixels group. These candidate facial features are verified whether the length and orientation of feature pairs are suitable for face geometry. In recognition step, pseudo-convex hull area of gray face image is defined which area includes feature triangle connecting two eyes and mouth. And random lattice line set are composed and laid on this convex hull area, and then 2D appearance of this area is represented. From these procedures, facial information of detected face is obtained and face DB images are similarly processed for each person class. Based on facial information of these areas, distance measure of match of lattice lines is calculated and face image is recognized using this measure as a classifier. This proposed detection and recognition algorithms overcome the constraints of previous approach [15], make real-time face detection and recognition possible, and guarantee the correct recognition irregardless of some pose variation of face. The usefulness at mobile robot application is demonstrated.

• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.353-362
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• 2005

최근 들어 바이모달 인식에 관한 연구가 활발히 진행되고 있다. 본 논문에서는 음성 정보와 얼굴정보를 이용하여 바이모달 시스템을 구현하였다. 얼굴인식은 얼굴 검출과 얼굴 인식 두 부분으로 나누어서 실험을 하였다. 얼굴 검출 단계에서는 AdaBoost를 이용하여 얼굴 후보 영역을 검출 한 뒤 PCA를 통해 특징 벡터 계수를 줄였다. PCA를 통해 추출된 특징 벡터를 객체 분류 기법인 SVM을 이용하여 얼굴을 검출 및 인식하였다. 음성인식은 MFCC를 이용하여 음성 특징 추출을 하였으며 HMM을 이용하여 음성인식을 하였다. 인식결과, 단일 인식을 사용하는 것보다 얼굴과 음성을 같이 사용하였을 때 인식률의 향상을 가져왔고, 잡음 환경에서는 더욱 높은 성능을 나타냈었다.

• 한국컴퓨터정보학회논문지
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• 제16권10호
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• pp.53-62
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• 2011

본 논문에서는 첨단 IT융,복합기술의 발달로 특수 기술로만 여겨졌던 영상객체인식 기술분야가 스마트-폰 기술의 발전과 더불어 개인 휴대용 단말기기로 발전하고 있다. 3D기반의 얼굴인식 검출기술은 객체인식 기술을 통하여 지능형 영상검출 인식기술기술로 진화되고 있음에 따라 영상인식을 통한 얼굴검출기술과 더불어 개발속도가 급속히 발전하고 있다. 본 논문에서는 휴먼인식기술을 기반으로 한 얼굴객체인식 영상검출을 통한 얼굴인식처리 기술의 인지 적용기술을 IP카메라에 적용하여 인가자의 입,출입등의 식별능력을 적용한 휴먼인식을 적용한 얼굴측정 기술에 대한 연구방안을 제안한다. 연구방안은 1)얼굴모델 기반의 얼굴 추적기술을 개발 적용하였고 2)개발된 알고리즘을 통하여 PC기반의 휴먼인식 측정 연구를 통한 기본적인 파라미터 값을 CPU부하에도 얼굴 추적이 가능하며 3)양안의 거리 및 응시각도를 실시간으로 추적할 수 있는 효과를 입증하였다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• 한국항행학회논문지
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• 제24권5호
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• pp.430-437
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• 2020

제안 모델은 얼굴 검출과 랜드마크 및 얼굴 인식 알고리즘을 이용하여 인공신경망으로 학습을 통해 얼굴 예측률과 인식률을 향상하는 모델을 구현하였다. 제안 모델은 특정 인물의 얼굴 영상에서 랜드마킹을 한 후, 기존에 학습된 Caffe 모델을 이용하여 얼굴검출과 임베딩 벡터 128D를 추출하였다. 학습은 기계학습 알고리즘인 SVM (support vector machine)과 DNN (deep neural network)을 구축하여 학습하였다. 얼굴인식은 학습된 모델을 이용하여 학습된 인물 중 다른 얼굴 영상으로 테스트하였다. 실험 결과, SVM 보다는 DNN으로 학습한 결과가 우수한 예측률과 인식률을 보였다. DNN의 중간층을 증가하게 되면 예측률은 높아지나 인식률이 감소하는 현상이 발생하였다. 이것은 인식하고자 하는 대상이 적음으로써 발생하는 과적합으로 판단된다. 제안 모델은 명확한 얼굴 영상을 추가하여 학습한 결과, 높은 예측률과 인식률의 결과를 얻을 수 있음을 확인할 수 있었다. 본 연구는 좀 더 많은 얼굴 영상 데이터를 이용함으로써 보다 효과적인 딥러닝 구축을 통해 보다 향상된 인식률과 예측률을 얻을 수 있을 것이다.