• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.565-567
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• 2022

질병을 앓고 있는 환자는 상태에 따라 병실, 주거지, 요양원 등 조호환경 내 생활 시 의료 인력의 지속적인 추적 및 관찰을 통해 신체에 이상이 생긴 경우 이를 감지하고, 신속하게 조치할 수 있도록 해야 한다. 의료 인력이 직접 환자를 확인하는 방법은 의료 인력의 반복적인 노동이 요구되며 실시간으로 환자를 확인해야 한다는 특성상 의료 인력이 상주해야 하기에 이는 곧, 의료 인력의 부족과 낭비로 이어진다. 해당 문제 해결을 위해 의료 인력을 대신하여 조호환경 내 환자의 상태를 실시간으로 모니터링할 수 있는 딥러닝 모델들이 연구되고 있다. 딥러닝 모델은 데이터의 수가 많을수록 강인한 모델을 설계할 수 있으며, 데이터셋의 배경, 객체의 특징 분포 등 다양한 조건에 영향을 받기 때문에 학습에 필요한 도메인을 가지는 많은 양의 전처리된 데이터를 수집해야 한다. 따라서, 조호환경 내 환자에 대한 데이터셋이 필요하지만, 공개된 데이터셋의 경우 양이 매우 적으며 이를 반전, 회전기법 등을이용할 경우 데이터의 수를 늘릴 수 있지만, 같은 분포의 특징을 가지는 데이터가 생성되기에 데이터 증강 기법을 단순하게 적용하면 딥러닝 모델의 과적합을 야기한다. 또한, 조호환경 내 이미지 데이터셋은 얼굴 노출과 같은 개인정보가 포함 될 수 있으며 이를 보호하기 위해 정보들을 비식별화 해야 한다는 문제점이 있다. 따라서 본 논문에서는 조호환경에서 수집된 데이터 증강을 위한 Style-Generative Adversarial Networks 기법을 적용하여 조호환경 데이터셋 수집에 효과적인 증강 기법을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.45-55
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• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1035-1045
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• 2017

Classification of age and gender has been carried out through different approaches such as facial-based and audio-based classifications. One of the limitations of facial-based methods is the reduced recognition rate over large distances, while another is the prerequisite of the faces to be located in front of the camera. Similarly, in audio-based methods, the recognition rate is reduced in a noisy environment. In contrast, gait-based methods are only required that a target person is in the camera. In previous works, the view point of a camera is only available as a side view and gait data sets consist of a standard gait, which is different from an ordinary gait in a real environment. We propose a feature extraction method using skeleton models from an RGB-D sensor by considering characteristics of age and gender using ordinary gait. Experimental results show that the proposed method could efficiently classify age and gender within a target group of individuals in real-life environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1120-1131
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• 2016

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker's activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.9-16
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• 2020

Pronunciation recognition systems that use only video information and ignore voice information can be applied to various customized services. In this paper, we develop a system that applies a Bayesian classifier to distinguish Korean vowels via lip shapes in images. We extract feature vectors from the lip shapes of facial images and apply them to the designed machine learning model. Our experiments show that the system's recognition rate is 94% for the pronunciation of 'A', and the system's average recognition rate is approximately 84%, which is higher than that of the CNN tested for comparison. Our results show that our Bayesian classification method with feature values from lip region landmarks is efficient on a small training set. Therefore, it can be used for application development on limited hardware such as mobile devices.

• Archives of Craniofacial Surgery
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• v.12 no.2
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• pp.86-92
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• 2011

Purpose: The purpose of this study was to investigate the possibility that a dynamic facial composite flap with sensory and motor nerves could be made available from donor facial composite tissue. Methods: The faces of 3 human cadavers were dissected. The authors studied the donor faces to assess which facial composite model would be most practicable. A "panorama facial flap" was excised from each facial skeleton with circumferential incision of the oral mucosa, lower conjunctiva and endonasal mucosa. In addition, the authors measured the available length of the arterial and venous pedicles, and the sensory nerves. In the recipient, the authors evaluated the time required to anastomose the vessels and nerve coaptations, anchor stitches for donor flaps, and skin stitches for closure. Results: In the panorama facial flap, the available anastomosing vessels were the facial artery and vein. The sensory nerves that required anastomoses were the infraorbital nerve and inferior alveolar nerve. The motor nerve requiring anstomoses was the facial nerve. The vascular pedicle of the panorama facial flap is the facial artery and vein. The longest length was 78 mm and 48 mm respectively. Sensation of the donor facial composite is supplied by the infraorbital nerve and inferior alveolar nerve. Motion of the facial composite is supplied by the facial nerve. Some branches of the facial nerve can be anastomosed, if necessary. Conclusion: The most practical facial composite flap would be a mid and lower face flap, and we proposed a panorama facial flap that is designed to incorporate the mid and lower facial skin with and the unique tissue of the lip. The panorama facial composite flap could be considered as one of the practicable basic models for facial allotransplantation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Smart Media Journal
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• v.11 no.2
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• pp.25-30
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• 2022

With the recent development of device performance such as smartphones, cameras, and video cameras, it has become possible to obtain human biometric information from images and photos. A German hacker group obtained human iris information from high-definition photos and revealed hacking into iris scanners on smartphones. As high-quality images and photos can be obtained with such advanced devices, the need for a suitable security system is also emerging. Therefore, in this paper, we propose a method of automatically masking human iris information in images and photos using Haar Cascades and Blur models from openCV. It is a technology that automatically masks iris information by recognizing a person's eye in a photo or video and provides the result. If this technology is used in devices and applications such as smartphones and zoom, it is expected to provide better security services to users.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.891-894
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• 1998

This paper introduces an automatic face region extraction method. This method consists of two part: face recognition and extraction of facial organs which are eye, eyebrow, nose and mouth. In first stage, we use genetic algorithms(GAs) to get face region in complex background. In second stage, we use Geometrical Face Model to textract eye, eyebrow, nose and mouth. In both stage, stochastic component is used to deal with the problems caused by had lighting condition. According to this value, blurring number is determined. Average Computation time is less than 1 sec, and using this method we can extract facial feature efficiently from several images which has different lightning condition.