• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.243-262
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• 2022

In this study, eye tracking was used for the appearance of the robot during the social robot design study. During the research, each part of the social robot was designated as AOI (Areas of Interests), and the user's attitude was measured through a design evaluation questionnaire to construct a design research model of the social robot. The data used in this study are Fixation, First Visit, Total Viewed, and Revisits as eye tracking indicators, and AOI (Areas of Interests) was designed with the face, eyes, lips, and body of the social robot. And as design evaluation questionnaire questions, consumer beliefs such as Face-highlighted, Human-like, and Expressive of social robots were collected and as a dependent variable was attitude toward robots. Through this, we tried to discover the mechanism that specifically forms the user's attitude toward the robot, and to discover specific insights that can be referenced when designing the robot.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.681-687
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• 2007

Face recognition technique is very popular for a personal information security and user identification in recent years. However, the face recognition system is very hard to be implemented due to the difficulty where change in illumination, pose and facial expression. In this paper, we consider that an illumination change causing the variety of face appearance, virtual image data is generated and added to the D-LDA which was selected as the most suitable feature extractor. A less sensitive recognition system in illumination is represented in this paper. This way that consider nature of several illumination directions generate the virtual training image data that considered an illumination effect of the directions and the change of illumination density. As result of experiences, D-LDA has a less sensitive property in an illumination through ORL, Yale University and Pohang University face database.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.875-882
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• 2008

Ear detection in an image processing is the one of the important area in biometrics. In this paper we propose a human ear detection algorithm with side face images. First, we search a face candidate area in an input image by using skin-color model and try to find an ear area based on Haar-like feature. Then, to verity whether it is the ear area or not, we use the template which is excellent object classification compare to recognize the characters in the plate. In this experiment, the proposed method showed that the processing speed is improved by 60% than previous works and the detection success rate is 92%.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.509-512
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• 2005

A human's effort to enjoy healthy life all life is increased, it is Sasang medicine that is receiving many interests. Sasang medicine person's constitution by 4 and behaved correct medicine arts in constitution. Therefore, In this paper, be going to propose a methodology for developing practitioner's intuition to objective equipment by visualize, measuring and quantize practitioner's a shape of the body and its countenance methods. For this, be going to extract characteristic elements, which are needed to assort Sasang constitution classification, from front and side face and distinguish four constitutions. Finally, usefulness of method proposed by an experiment world prove.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.813-816
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• 2005

Ear detection is an important part of an non-invasive ear recognition system. In this paper we propose human ear detection from side face images. The proposed method is made by imitating the human recognition process using feature information and color information. First, we search face candidate area in an input image by using 'skin-color model' and try to find an ear area based on edge information. Then, to verify whether it is the ear area or not, we use the SVM (Support Vector Machine) based on a statistical theory. The method shows high detection ratio in indoors environment with stable illumination.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.43-48
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• 2013

Adaboost is widely used for Haar-like feature boosting algorithm in Face Detection. It shows very effective performance on single distribution model. But when detecting front and side face images at same time, Adaboost shows it's limitation on multiple distribution data because it uses linear combination of basic classifier. This paper suggest the HDCT, modified decision tree algorithm for Haar-like features. We still tested the performance of HDCT compared with Adaboost on multiple distributed image recognition.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.75-80
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• 2021

Artificial intelligence is becoming an important part of our lives providing incredible benefits. In this respect, facial expression recognition has been one of the hot topics among computer vision researchers in recent decades. Classifying small dataset of low resolution images requires the development of a new small scale deep CNN model. To do this, we propose a method suitable for small datasets. Compared to the traditional deep CNN models, this model uses only a fraction of the memory in terms of total learnable weights, but it shows very similar results for the FER2013 and FERPlus datasets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.913-921
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• 1993

Texture mapping is mostly used as an image synthesis method in the model-based coding system. An image synthesis using this method uses only the texture information of a front face-view. Therefore, when the model is rotated, texture mapping may produce an awkward image in point of shading. In this paper. a new texture mapping method considering shading effect is studied, and also the ear's wireframe and changes of hair are suplemented for the relation. The experimental results show that the proposed method yields the synthesized images with reasonably natural quality.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.831-834
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• 2005

실시간 안면 생체정보 추출 알고리즘은 다양한 멀티미디어 및 보안 시스템에 적용이 가능하다. 그러나 추출율과 시간 이득이라는 측면에서 모두 만족하는 알고리즘은 제안된 사례가 극히 드물며, 그 결과 역시 만족스럽지 못한 경우가 많았다. 본 연구에서는 스킨 칼라 모델을 기반으로 하여 높은 시간 이득을 보장하는 동영상 기반의 실시간 얼굴 영역 추출에 대한 알고리즘을 제시하고자 한다.