• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2341-2350
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• 2012

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver's face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver's face can be detected rapidly and precisely.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• 패션비즈니스
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• 제12권5호
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• pp.77-93
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• 2008

The purpose of the study was to examine make-up expression elements of horror characters as an evil spirit, a vampire, a zombie, a monster and a psycho murderer through make-up, focused on horror movies of the United States after1960s. First, an evil spirit was expressed that the pupil grew dim without a focus and the skin was torn by going bad and rot completely. Also, it was expressed that its teeth was changed to pointed canine teeth as a wild beast and its pale and bloodless face showed blood vessels clearly. To express its bloodless and pale skin, air brush was swept into the skin over several times. Its canine tooth was made acryl powder into its shape and was harden to make its shape smooth. Also, it was colored to show people like the real thing. Second, a vampire was expressed by make-up elements as long nails, a sharp canine tooth, a pale skin with a blood vessel and dead eyes. Its rough skin and long nails were manufactured with latex foam, which was colored like the skin. Third, a monster image as a werewolf was expressed by make-up elements as fur, sharp teeth, changed nails and toenails, a rough skin and a face changed to a wolf. Rubber mask and rubber body suit were manufactured as a make-up of special character and so all its body was changed. Fourth, a psycho murderer and body-deformed man were expressed by make-up elements as a distorted mask, swelled-up and deformed skull and face form, terribly deformed body, discolored and hang down skin and teeth with an indeterminate form. Its body and face were manufactured by the foam latex technology according to make-up design as a make-up of special character.

• 감성과학
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• 제17권4호
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• pp.61-70
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• 2014

로봇과 인간의 상호작용에서 언어에 의한 정보 전달은 상호작용의 한계가 있으므로, 더욱 원활하고 효율적인 의사소통과 나아가 로봇의 감정 표현까지 구현하기 위해서는 비언어적 커뮤니케이션이 필요하다. 본 연구에서는 쇼핑을 지원하는 로봇을 전제로 하여 쇼핑 행태에 따른 7가지 비언어적 정보를 도출하였다. 도출된 비언어적 정보의 요소로서 표정을 선정하고, 2D 분석을 통하여 얼굴 구성요소를 코드화 하였다. 얼굴 구성요소의 코드를 조합한 3D 애니메이션을 이용하여 비언어적 정보의 표현에 대한 유의성을 분석하였다. 분석 결과, 제안된 비언어적 정보의 표현 방법은 높은 수준의 유의성을 보여 비언어적 정보 연구의 기초자료로서 활용 가능성이 확인되었다. 다만, '당황'의 경우 코드화된 얼굴 구성 요소의 모양 적용에 한계가 있으며 보다 체계적 연구가 요구된다.

• 정보처리학회논문지B
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• 제12B권7호
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• pp.795-802
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• 2005

본 논문에서는 일곱 가지의 기본적인 감정 정보를 자동으로 파악하고 얼굴을 PDA 상에서 렌더링할 수 있는 얼굴 표정의 인식 및 합성 시스템을 제시한다. 얼굴 표정 인식을 위해서 먼저 카메라로부터 획득한 영상으로부터 얼굴 부분을 검출한다. 그리고 나서 기하학적 또는 조명으로 인한 보정을 위해 정규화 과정을 거친다. 얼굴 표정을 분류하기 위해서는 Gabor wavelets 방법을 enhanced Fisher 모델과 결합하여 사용할 때가 가장 좋은 결과를 보였다. 본 표정 분류에서는 일곱 가지 감정 가중치가 결과로 제시되고, 그러한 가중 정보는 모바일 네트웍을 통하여PDA 상으로 전송되어 얼굴 표정 애니메이션에 이용되어진다. 또한 본 논문에서는 고유한 얼굴 캐릭터를 가진 3차워 아바타를 생성하기 위하여 카툰 쉐이딩 기법을 채택하였다. 실험 결과 감정 곡선을 이용한 얼굴 표정 애니메이션은 선형 보간법 보다 감정 변화의 타이밍을 표현하는데 더 효과적인 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.4903-4929
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• 2018

In this study, a fully automatic pose and expression invariant 3D face alignment algorithm is proposed to handle frontal and profile face images which is based on a two pass course to fine alignment strategy. The first pass of the algorithm coarsely aligns the face images to an intrinsic coordinate system (ICS) through a single 3D rotation and the second pass aligns them at fine level using a minimum nose tip-scanner distance (MNSD) approach. For facial recognition, multi-view faces are synthesized to exploit real 3D information and test the efficacy of the proposed system. Due to optimal separating hyper plane (OSH), Support Vector Machine (SVM) is employed in multi-view face verification (FV) task. In addition, a multi stage unified classifier based face identification (FI) algorithm is employed which combines results from seven base classifiers, two parallel face recognition algorithms and an exponential rank combiner, all in a hierarchical manner. The performance figures of the proposed methodology are corroborated by extensive experiments performed on four benchmark datasets: GavabDB, Bosphorus, UMB-DB and FRGC v2.0. Results show mark improvement in alignment accuracy and recognition rates. Moreover, a computational complexity analysis has been carried out for the proposed algorithm which reveals its superiority in terms of computational efficiency as well.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.363-366
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• 2005

In this paper, for detecting human faces under variations of lighting condition and facial expression, distance ranking feature and detection algorithm based on the feature are proposed. Distance ranking is the intensity ranking of a distance transformed image. Based on statistically consistent edge information, distance ranking is robust to lighting condition change. The proposed detection algorithm is a matching algorithm based on FFT and a solution of discretization problem in the sliding window methods. In experiments, face detection results in the situation of varying lighting condition, complex background, facial expression change and partial occlusion of face are shown

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.917-924
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• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.