• Journal of Korea Multimedia Society
• /
• 제8권1호
• /
• pp.34-45
• /
• 2005

In this paper, a novel implementation of a person verification system based on depth-weighted Hausdorff distance (DWHD) using the surface curvature of the face is proposed. The definition of Hausdorff distance is a measure of the correspondence of two point sets. The approach works by finding the nose tip that has a protrusion shape on the face. In feature recognition of 3D face image, one has to take into consideration the orientated frontal posture to normalize after extracting face area from original image. The binary images are extracted by using the threshold values for the curvature value of surface for the person which has differential depth and surface characteristic information. The proposed DWHD measure for comparing two pixel sets were used, because it is simple and robust. In the experimental results, the minimum curvature which has low pixel distribution achieves recognition rate of 98% among the proposed methods.

• Journal of information and communication convergence engineering
• /
• 제11권3호
• /
• pp.207-215
• /
• 2013

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person's head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

• Journal of the Korea Safety Management & Science
• /
• 제24권1호
• /
• pp.31-38
• /
• 2022

The most common symptoms of COVID-19 are high fever, cough, headache, and fever. These symptoms may vary from person to person, but checking for "fever" is the government's most basic measure. To confirm this, many facilities use thermographic cameras. Since the previously developed thermographic camera measures body temperature one by one, it takes a lot of time to measure body temperature in places where many people enter and exit, such as multi-use facilities. In order to prevent malfunctions and errors and to prevent sensitive personal information collection, this research team attempted to develop a facial recognition thermographic camera. The purpose of this study is to compensate for the shortcomings of existing thermographic cameras with disaster safety IoT integrated solution products and to provide quarantine systems using advanced facial recognition technologies. In addition, the captured image information should be protected as personal sensitive information, and a recent leak to China occurred. In order to prevent another case of personal information leakage, it is urgent to develop a thermographic camera that reflects this part. The thermal imaging camera system based on facial recognition technology developed in this study received two patents and one application as of January 2022. In the COVID-19 infectious disease disaster, 'quarantine' is an essential element that must be done at the preventive stage. Therefore, we hope that this development will be useful in the quarantine management field.

• Research in Community and Public Health Nursing
• /
• 제30권3호
• /
• pp.295-306
• /
• 2019

Purpose: There has been a growing recognition that person-centered care enhances the quality of life of nursing home residents with dementia. This study was conducted to develop a person-centered dementia care online education program for direct care staff in long-term care facilities. Methods: Delphi method with expert group was used to validate contents. We developed 61 draft items based on literature review. Twenty experts participated in consecutive three round surveys including 5-point Likert scale questions and open-ended questions. Based on experts' opinions, the content validity ratio for content validity and the coefficient of variation for stability were calculated. Results: Three-round Delphi surveys and additional feedback from the expert panel established a consensus of core contents: 1) dementia (7 categories), 2) person-centered care (6 categories), 3) communication (8 categories), and 4) behavioral and psychological symptoms of dementia (6 categories). Specific sub-categories in each category were differentiated according to the job qualifications (65 sub-categories for registered nurses, 64 sub-categories for nursing aids, and 41 sub-categories for personal care workers). Conclusion: This delphi study identified person-centered dementia education curricula, in which the person-centered approach should be a key policy priority in Korean long-term care system. Now it is urgently needed to develop education programs utilizing online platforms that enable efficient and continuous learning for long-term care staff, which can contribute to behavior changes in the person-centered dementia care approach and improvement of care quality in long-term care facilities.

• The Journal of the Acoustical Society of Korea
• /
• 제28권3E호
• /
• pp.109-117
• /
• 2009

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

• The Journal of the Korea institute of electronic communication sciences
• /
• 제9권3호
• /
• pp.343-350
• /
• 2014

Object recognition is one of the key technologies of the monitoring system for the prevention of crimes diversified the intelligent. The height is one of the physical information of the person, it may be important information to confirm the identity with physical characteristics of the subject has. In this paper, we provide a method of measuring the height that utilize RGB-Depth camera, the Kinect. Given that in order to measure the height of a person, and know the height of Kinect, by using the depth information of Kinect the distance to the head and foot of Kinect, estimating the height of a person. The proposed method throughout the experiment confirms that it is effective to estimate the height of a person in the room.

• Journal of Information Processing Systems
• /
• 제14권5호
• /
• pp.1114-1135
• /
• 2018

A multiple classification system based on a new boosting technique has been approached utilizing different biometric traits, that is, color face, iris and eye along with fingerprints of right and left hands, handwriting, palm-print, gait (silhouettes) and wrist-vein for person authentication. The images of different biometric traits were taken from different standard databases such as FEI, UTIRIS, CASIA, IAM and CIE. This system is comprised of three different super-classifiers to individually perform person identification. The individual classifiers corresponding to each super-classifier in their turn identify different biometric features and their conclusions are integrated together in their respective super-classifiers. The decisions from individual super-classifiers are integrated together through a mega-super-classifier to perform the final conclusion using programming based boosting. The mega-super-classifier system using different super-classifiers in a compact form is more reliable than single classifier or even single super-classifier system. The system has been evaluated with accuracy, precision, recall and F-score metrics through holdout method and confusion matrix for each of the single classifiers, super-classifiers and finally the mega-super-classifier. The different performance evaluations are appreciable. Also the learning and the recognition time is fairly reasonable. Thereby making the system is efficient and effective.

• Journal of Korean society of Dental Hygiene
• /
• 제16권6호
• /
• pp.909-918
• /
• 2016

Objectives: The purpose of the study was to investigate the correlation between self-esteem, self-efficacy, and awareness for disability in dental hygiene students. Methods: A self-reported questionnaire was completed by 521 dental hygiene students from 5 universities in Gwangju and Jeonnam frm June 13 to July 16, 2016. The questionnaire consisted of self-esteem by Rosenberg (10 items), self-efficacy by Schwarzer (7 items), and the negative awareness for disabled person by Siller (24 items) using Likert 5 point scale. Results: Those who did volunteer activity for disabled person showed high self-esteem and self-efficacy than those who did not. Students having disability-related training experience had high self-esteem (p<0.05). Higher the self-esteem was, higher the self-efficacy was (r=0.655). Higher the self-efficacy was, the lower the negative awareness was (r=-0.142). Higher self-esteem enhanced the positive awareness for the disabled person (r=-0.206)(p<0.01). Conclusions: The systematic curriculum development for the disabled person recognition improvement should be made in order to enhance self-esteem and self-efficacy of the dental hygiene students.

• Journal of the Korean Society of Clothing and Textiles
• /
• 제32권5호
• /
• pp.729-740
• /
• 2008

The purposes of this study are to identify service quality elements using Kano's theory, and to examine differences in service quality characteristics among apparel store types. Women over 20 years-old from metropolitan areas in South Korea participated in the study and a quota sampling method was used. The questionnaire was composed of three sections; importance of quality, degree of satisfaction, and demographic factors. Data from 525 questionnaires were used for the statistical analysis. The results were as follows: First, four factors of service quality(sales person, variety of goods, policy, and facilities) were identified. According to Kano's quality elements, sales person was categorized into both one-dimensional quality and must-be quality, and variety of goods, policy, and facilities categorized into attractive quality. Second, sales person, in all store types, was included in one-dimension quality elements and sales person and variety of goods, in local store, were included in attractive quality element. Service policy, in chain store and local store, was included in attractive quality element. Findings of this study provide both industry and academic researchers with a guide to increase customer satisfaction in the store service strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권6호
• /
• pp.2767-2780
• /
• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.