• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.185-189
• /
• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1244-1248
• /
• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• 정보처리학회논문지B
• /
• 제9B권6호
• /
• pp.853-862
• /
• 2002

얼굴 검출은 디지털화 된 임의의 정지 영상 혹은 연속된 영상으로부터 얼굴 존재유무를 판단하고, 얼굴이 존재할 경우 영상 내 얼굴의 위치, 방향, 크기 등을 알아내는 기술로 정의된다. 이러한 얼굴 검출은 얼굴 인식이나 표정인식, 헤드 제스쳐 등의 기초 기술로서해당 시스템의 성능에 매우 중요한 변수 중에 하나이다. 그러나 영상 내의 얼굴은 표정, 포즈, 크기, 빛의 방향 및 밝기, 안경, 수염 등의 환경적 변화로 인해 얼굴 모양이 다양해지므로 정확하고 빠른 검출이 어렵다. 따라서 본 논문에서는 오류-역전파 신경망을 사용하여 몇가지 환경적 조건을 극복한 정확하고 빠른 얼굴 검출 방법을 제안한다. 제안된 방법은 표정과 포즈, 배경에 무관하게 얼굴을 검출하면서도 빠른 검출이 가능하다. 이를 위해 신경망을 이용하여 얼굴 검출을 수행하고, 검색 영역의 축소와 신경망 계산 시간의 단축으로 검출 응답 시간을 빠르게 하였다. 검색 영역의 축소는 영상 내 피부색 영역의 분할과 차영상을 이용하였고, 주성분 분석을 통해 신경망의 입력 백터를 축소시킴으로써 신경망 수행 시간과 학습 시간을 단축시켰다. 또, 추출된 얼굴 영상에서 포즈를 추정하고 눈 영역을 검출함으로써 얼굴 정보의 사용에 있어 보다 많은 정보를 추출할 수 있도록 하였다. 얼굴 검출 실험은 마할라노비스 거리를 사용하여 검출된 영상의 얼굴 여부를 판정하고, 성공률과 시간을 측정하였다. 정지 영상과 동영상에서 모두 실험하였으며, 피부색 영역의 분할을 사용할 경우 입력 영상의 칼라 설정의 유무에 다른 검출 성공률의 차를 보였다. 포즈 실험도 같은 조건에서 수행되었으며, 눈 영역의 검출은 안경의 유무에 다른 실험 결과를 보였다. 실험 결과 실시간 시스템에 사용 가능한 수준의 검색률과 검색 시간을 보였다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2006년도 하계종합학술대회
• /
• pp.267-268
• /
• 2006

Advanced TV makes the life more convenient for the viewers and it is based on the recognition technology. In this paper, we propose the implementation of face tracking system for efficient face recognition in intelligent digital TV. To recognize the face, face detection should be performed earlier. We use the motion information to track the face. Continuous face tracking is possible by using continuous detected face region and motion information. Thus the computational complexity of the recognition module in the whole system can be reduced.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2002년도 춘계종합학술대회
• /
• pp.134-138
• /
• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Journal of information and communication convergence engineering
• /
• 제5권1호
• /
• pp.50-53
• /
• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• 디지털콘텐츠학회 논문지
• /
• 제13권2호
• /
• pp.189-195
• /
• 2012

본 논문에서는 휴먼 인지를 위한, 근적외선 얼굴 영상에서의 얼굴 검출 방법이 제안된다. 에지의 강도와 방향에 기반한 에지 히스토그램이 근적외선 영상으로부터 얼굴을 검출하기 위해 사용되었다. 조명변화에 강인하기 때문에, 제안된 에지 히스토그램은 얼굴을 효과적으로 표현하고 구별한다. 얼굴 검출을 위한 분류기로서는 SVM(Support Vector Machine)을 사용하였으며 제안한 방법은 ULBP(Uniform Local Binary Pattern)보다 적은 피쳐 개수를 가지면서도 에러율 측면에서, ULBP의 경우보다 나은 성능을 나타내었다.

• 한국멀티미디어학회논문지
• /
• 제12권6호
• /
• pp.804-814
• /
• 2009

In this paper, a novel system for avatar motion controlling by tracking face is presented. The system is composed of three main parts: firstly, LCS (Local Cluster Searching) method based face feature detection algorithm, secondly, HMM based feature points recognition algorithm, and finally, avatar controlling and animation generation algorithm. In LCS method, face region can be divided into many small piece regions in horizontal and vertical direction. Then the method will judge each cross point that if it is an object point, edge point or the background point. The HMM method will distinguish the mouth, eyes, nose etc. from these feature points. Based on the detected facial feature points, the 3D avatar is controlled by two ways: avatar orientation and animation, the avatar orientation controlling information can be acquired by analyzing facial geometric information; avatar animation can be generated from the face feature points smoothly. And finally for evaluating performance of the developed system, we implement the system on Window XP OS, the results show that the system can have an excellent performance.

• Journal of Electrical Engineering and Technology
• /
• 제13권2호
• /
• pp.953-960
• /
• 2018

In recently, eye blink recognition, and face recognition are very popular and promising techniques. In some cases, people can use the photos and face masks to hack mobile security systems, so we propose an eye blinking detection, which finds eyes through the proportion of human face. The proposed method detects the movements of eyeball and the number of eye blinking to improve face recognition for screen unlock on the mobile devices. Experimental results show that our method is efficient and robust for the screen unlock on the mobile devices.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -2
• /
• pp.1252-1255
• /
• 2002

In this paper, face recognition algorithm, by using skin color information of HSI color coordinate collected from face images, elliptical mask, fratures of face including eyes, nose and mouth, and geometrical feature vectors of face and facial angles, is proposed. The proposed algorithm improved face region extraction efficacy by using HSI information relatively similar to human's visual system along with color tone information about skin colors of face, elliptical mask and intensity information. Moreover, it improved face recognition efficacy with using feature information of eyes, nose and mouth, and Θ1(ACRED), Θ2(AMRED) and Θ 3(ANRED), which are geometrical face angles of face. In the proposed algorithm, it enables exact face reading by using color tone information, elliptical mask, brightness information and structural characteristic angle together, not like using only brightness information in existing algorithm. Moreover, it uses structural related value of characteristics and certain vectors together for the recognition method.