• Proceedings of the ESK Conference
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• 1996.10a
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• pp.59-65
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• 1996

인간의 얼굴 표정은 인간의 감성이 가장 잘 나타나는 부분이다 . 따라서 전통적으로 인간의 표정을 감 성과 연관 지어 연구하려는 많은 노력이 있어 왔다. 최근에는 얼굴 온도 변화를 측정하는 방법, 근전도(EMG; Electromyography)로 얼굴 근육의 움직임을 측정하는 방법, 이미지나 동작분석에 의한 얼굴 표정의 연구가 가능 하게 되었다. 본 연구에서는 인간의 얼굴 표정 변화를 3차원 동작분석 장비를 이용하여 측정하였다. 얼굴 표정 의 측정을 위해 두가지의 실험을 계획하였는데, 첫번 째 실험에서는 피실험자들로 하여금 웃는 표정, 놀라는 표정, 화난 표정, 그리고 무표정 등을 짓게 한 후 이를 측정하였으며, 두번째 실험에스는 코미디 영화와 공포 영화를 피 실험자들에게 보여 주어 피실험자들의 표정 변화를 측정하였다. 5명의 성인 남자가 실험에 참여하였는데, 감성을 일으킬 수 있는 적절한 자극 제시를 못한 점 등에서 처음에 기도했던 6개의 기본 표정(웃음, 슬픔, 혐오, 공포, 화남, 놀람)에 대한 모든 실험과 분석이 수행되지 못했다. 나머지 부분을 포함한 정교한 실험 준비가 추후 연구 에서 요구된다. 이러한 연구는 앞으로 감성공학, 소비자 반응 측정, 컴퓨터 애니메이션(animation), 정보 표시 장치(display) 수단으로서 사용될 수 있을 것이다.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.49-50
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• 2018

본 논문은 깊이 이미지 이용한 페이셜 애니메이션 제작 방법을 소개한다. iPhone X의 true depth카메라를 사용하여 사람 얼굴의 심도를 정확하게 파악하고, 균등하게 분산된 도트를 통해 얼굴의 모든 표정변화를 모바일 데이터로 기록하여, 페이셜 애니메이션을 제작하는 제작한다. 본문에서의 방식은, 기존 페이셜 애니메이션 제작 과정에서의 rigging 부분을 생략하여, 기록된 얼굴 표정 데이터를 3D 모델링에 바로 전달할 수 있다. 이런 방식을 통해 전체 페이셜 애니메이션 제작 과정을 단축시켜, 제작 방법을 더욱 간단하고 효율적이게 하였다.

• Science of Emotion and Sensibility
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• v.9 no.2
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• pp.101-109
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• 2006

If a salesperson is able to juage consumers' purchasing preferences by looking at their eyebrows shapes, he might be able to set up his sales strategies that will be helpful for the sales. In order to set up this kind of strategy, the difference of purchasing preferences in relation to the eyebrows shapes must be defined clearly. The purpose of this study is to analyze the difference of purchasing preferences according to eyebrows shapes. We group eyebrows shapes into five classes in physiognomy, and Analyze the difference of purchasing preferences according to eyebrows shapes. The result shows that compared with people with rising tails of eyebrows, people with declined ones prefer common, simple, gentle and noble clothing image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.127-133
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• 2021

In this study, an augmented reality-based make-up tool was built to analyze the user's face shape based on face-type reference model data and to provide virtual makeup by providing face-type makeup. To analyze the face shape, first recognize the face from the image captured by the camera, then extract the features of the face contour area and use them as analysis properties. Next, the feature points of the extracted face contour area are normalized to compare with the contour area characteristics of each face reference model data. Face shape is predicted and analyzed using the distance difference between the feature points of the normalized contour area and the feature points of the each face-type reference model data. In augmented reality-based virtual makeup, in the image input from the camera, the face is recognized in real time to extract the features of each area of the face. Through the face-type analysis process, you can check the results of virtual makeup by providing makeup that matches the analyzed face shape. Through the proposed system, We expect cosmetics consumers to check the makeup design that suits them and have a convenient and impact on their decision to purchase cosmetics. It will also help you create an attractive self-image by applying facial makeup to your virtual self.

• Journal of the Korea society of information convergence
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• v.1 no.1
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• pp.17-24
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• 2008

Video camera play very important roles for preventing many kinds of crimes and resolving those crime affairs. But in the case of recording image of a specific person far from the CCTV, the original image needs to be enlarged and recovered in order to identify the person more obviously. Interpolation is usually used for the enlargement and recovery of the image in this case. However, it has a certain limitation. As the magnification of enlargement is getting bigger, the quality of the original image can be worse. This paper uses FOP(Facial Definition Parameter) proposed by the MPEG-4 SNHC FBA group and introduces a new algorithm that uses face outline information of the original image based on the FOP, which makes it possible to recover better than the known methods until now.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.617-619
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• 2018

In this paper, We suggest a face image generating GAN model which is improved by an additive discriminator. This discriminator is trained to be specialized in preventing frequent mistake of generator. To verify the model suggested, we used $^*Inception$ score. We used 155,680 images of $^*celebA$ which is frontal face. We earned average 1.742p at Inception score and it is much better score compare to previous model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.200-203
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• 2021

This paper introduces a smart mirror technology that recognizes a person's facial expressions through image classification among several artificial intelligence technologies and presents them in a mirror. 5 types of facial expression images are trained through artificial intelligence. When someone looks at the smart mirror, the mirror recognizes my expression and shows the recognized result in the mirror. The dataset fer2013 provided by kaggle used the faces of several people to be separated by facial expressions. For image classification, the network structure is trained using convolution neural network (CNN). The face is recognized and presented on the screen in the smart mirror with the embedded board such as Raspberry Pi4.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1094-1097
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• 2010

생체 인식 기술 중 얼굴 인식과 눈동자 인식을 활용하여 기존의 얼굴 인식 내지 지문 인식만을 사용했을 때 보다 좀 더 인터렉티브한 인증 환경을 제공함으로써 복사한 이미지를 이용해 인증 체계를 회피할 수 있는 가능성을 줄였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.459-460
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• 2021

본 논문에서는 BEGAN을 활용한 한국인 얼굴 데이터 생성을 위한 최적의 Hyper Parameter를 제안한다. 연구에서는 GAN의 발전된 모델인 BEGAN을 이용한다. 위의 모델을 작성하기 위하여 본 논문에서는 Anaconda 기반의 Jupyter Notebook에서 Python Tensorflow 모델을 작성하여 테스트하고, 만들어진 모델을 FID를 통해 모델의 성능을 비교한다. 본 연구에서는 제안하는 방법들을 통해서 만들어진 모델을 이용해 한국인 얼굴 데이터를 구하고, 생성된 이미지에 대한 정량적인 평가를 진행한다.