• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.373-376
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• 2013

Nowadays, eyes detection is required and considered as the most important step in several applications, such as eye tracking, face identification and recognition, facial expression analysis and iris detection. This paper presents the eyes detection in facial images using Harris corner detection. Firstly, Haar-like features for face detection is used to detect a face region in an image. To separate the region of the eyes from a whole face region, the projection function is applied in this paper. At the last step, Harris corner detection is used to detect the eyes location. In experimental results, the eyes location on both grayscale and color facial images were detected accurately and effectively.

• 전자공학회논문지B
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• 제33B권8호
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• pp.114-123
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• 1996

In this paper, we propose an automatic recognition algorithm using the object centroid of a facial image. First, we separate the facial image from the background image using the chroma-key technique and we find the centroid of the separated facial image. Second, we search nose in the facial image based on knowledge of human faces and the coordinate of the object centroid and, we calculate 17 feature parameters automatically. Finally, we recognize the facial image by using feature parameters in the neural networks which are trained through error backpropagation algorithm. It is illustrated by experiments by experiments using the proposed recogniton system that facial images can be recognized in spite of the variation of the size and the position of images.

• Imaging Science in Dentistry
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• 제48권2호
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• pp.111-119
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• 2018

Purpose: This study was conducted to evaluate the accuracy of linear measurements of 3-dimensional (3D) images generated by cone-beam computed tomography (CBCT) and facial scanning systems, and to assess the effect of scanning parameters, such as CBCT exposure settings, on image quality. Materials and Methods: CBCT and facial scanning images of an anthropomorphic phantom showing 13 soft-tissue anatomical landmarks were used in the study. The distances between the anatomical landmarks on the phantom were measured to obtain a reference for evaluating the accuracy of the 3D facial soft-tissue images. The distances between the 3D image landmarks were measured using a 3D distance measurement tool. The effect of scanning parameters on CBCT image quality was evaluated by visually comparing images acquired under different exposure conditions, but at a constant threshold. Results: Comparison of the repeated direct phantom and image-based measurements revealed good reproducibility. There were no significant differences between the direct phantom and image-based measurements of the CBCT surface volume-rendered images. Five of the 15 measurements of the 3D facial scans were found to be significantly different from their corresponding direct phantom measurements(P<.05). The quality of the CBCT surface volume-rendered images acquired at a constant threshold varied across different exposure conditions. Conclusion: These results proved that existing 3D imaging techniques were satisfactorily accurate for clinical applications, and that optimizing the variables that affected image quality, such as the exposure parameters, was critical for image acquisition.

• 대한한의학회지
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• 제44권4호
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• pp.136-149
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• 2023

Objectives: Facial diagnosis is an important part of clinical diagnosis in traditional East Asian Medicine. In this paper, we proposed a model to quantitatively classify cold sensitivity using a fully automated facial image analysis system. Methods: We investigated cold sensitivity in 452 subjects. Cold sensitivity was determined using a questionnaire and the Cold Pattern Score (CPS) was used for analysis. Subjects with a CPS score below the first quartile (low CPS group) belonged to the cold non-sensitivity group, and subjects with a CPS score above the third quartile (high CPS group) belonged to the cold sensitivity group. After splitting the facial images into train/validation/test sets, the train and validation set were input into a convolutional neural network to learn the model, and then the classification accuracy was calculated for the test set. Results: The classification accuracy of the low CPS group and high CPS group using facial images in all subjects was 76.17%. The classification accuracy by sex was 69.91% for female and 62.86% for male. It is presumed that the deep learning model used facial color or facial shape to classify the low CPS group and the high CPS group, but it is difficult to specifically determine which feature was more important. Conclusions: The experimental results of this study showed that the low CPS group and the high CPS group can be classified with a modest level of accuracy using only facial images. There was a need to develop more advanced models to increase classification accuracy.

• Journal of information and communication convergence engineering
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• 제9권2호
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• pp.207-211
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• 2011

This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images. Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest, especially on the local features on the face. This is a very unique attribute needed to extract special features around the facial components like eyebrows, eyes, mouth and nose. The Covariance matrices computed on Gabor filtered faces are adopted as the feature representation for face recognition. Geodesic distance measure is used as a matching measure and is preferred for its global consistency over other methods. Geodesic measure takes into consideration the position of the data points in addition to the geometric structure of given face images. The proposed method is invariant and robust under rotation, pose, or boundary distortion. Tests run on random images and also on publicly available JAFFE and FRAV3D face recognition databases provide impressively high percentage of recognition.

• ETRI Journal
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• 제44권4호
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• 디지털콘텐츠학회 논문지
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• 제8권3호
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• pp.329-339
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• 2007

본 논문은 연속적으로 입력되는 동영상에서 시공간 정보를 이용하여 다양한 조명환경에서도 실시간 적용이 가능한 얼굴영역 검출기법을 제안한다. 제안한 알고리즘은 연속된 두개의 연속 영상에서 에지 차영상을 구하고 연속적으로 입력되는 영상과의 차분 누적영상을 통해 초기 얼굴영역을 검출한다. 초기 얼굴영역으로부터 외부 조명의 영향을 없애기 위해, 검출된 초기 얼굴영역의 수평 프로파일을 이용하여 수직 방향으로 객체영역을 이분하며, 각각의 객체영역에 관해 주색상 밝기를 구한다. 배경과 잡음 성분을 제거한 후, 분할된 얼굴영역을 통합한 주색상 밝기 분포를 이용하여 타원으로 근사화 함으로써 정확한 얼굴의 기울기와 영역을 실시간으로 계산한다. 제안된 방법은 다양한 조명조건에서 얻어진 동영상을 이용하여 실험되었으며 얼굴의 좌 우 기울기가 $30^{\circ}$이하에서 우수한 얼굴영역 검출 성능을 보였다.

• 복식
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• 제64권3호
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• pp.62-76
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• 2014

The purpose of this study is to classify the facial images and analyze shape characteristics of Korean women in their 20s. Previous research and survey were used for the study, the surveys targeted 220 university students in their 20s. The subjects of the experiment were 20-24 year-old Korean women. SPSS 12.0 statistics program was used to analyze the results, and factor analysis, Cronbach's ${\alpha}$ reliability analysis, and multidimensional scaling(MDS) were executed. The results of the study are as follows: First, the facial image types of Korean women in their 20s were classified into 4 categories as 'Youthfulness', 'Classiness', 'Friendliness', and 'Activeness'. Second, the multi-dimensional scaling method was performed and two orthogonal dimensions for the facial image of the Korean women were suggested: strong - soft and classy-friendly. Third, by analyzing the basic statistics concerning the structural characteristics of facial image of Korean women, there were differences in structural characteristics that form the facial images. Especially, significant difference appeared in items related forehead, eyebrows, eyes and jaw.

• 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.

• 한국통신학회논문지
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• 제19권4호
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• pp.614-623
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• 1994

얼굴을 마주보며 인간끼리 대화하는 것처럼 인간과 자연스럽게 대화할 수 있는 휴먼인터페이스를 실현하기 위해서는 임성의 합성과 얼굴영상의 합성이 필요하다. 본 논문은 얼굴영상의 합성을 대상으로 한다. 얼굴영상의 합성에서는 표정변화와 입모양의 변화를 3차원적으로 실현하기 위하여 얼굴의 3차원 형상모델을 이용한다. 얼굴의 3차원 모델을 얼굴 근육의 움직임에 따라 변형하므로서 다양한 얼굴표정과 음절에 어울리는 입모양을 합성한다. 우리말에서 자모의 결합으로 조합가능한 음절은 14,364자에 이른다. 이 음절에 대한 입모양의 대부분은 모음에 따라 형성되고, 일부가 자음에 따라 달라진다. 그러므로, 음절에 어울리는 입모양의 변형규칙을 정하기 위해, 이들을 모두 조사하여 모든 음절을 대표할 수 있는 입모양패턴을 모음과 자음에 따란 분류한다. 그 결과, 자음에 영향을 받는 2개의 패턴과 모음에 의한 8개의 패턴, 총 10개의 패턴으로 입모양을 분류할 수 있었다. 나아가서, 분류된 입모양패턴의 합성규칙을 얼굴근육의 움직임을 고려하여 정한다. 이와같이 분류된 10개의 입모양패턴으로 모든 음절에 대한 입모양을 합성할 수 있고, 얼굴근육의 움직임을 이용하므로써 다양한 표정을 지으면서 말하는 자연스런 얼굴영상을 합성할 수 있었다.