• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.163-172
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• 2005

When tracking a human face in the moving pictures with complex background under irregular lighting conditions, the detected face can be larger including background or smaller including only a part of the face. Even background can be detected as a face area. To solve these problems, this paper proposes a new face tracking method using a block difference image and a Kalman estimator. The block difference image allows us to detect even a small motion of a human and the face area is selected using the skin color inside the detected motion area. If the pixels with skin color inside the detected motion area, the boundary of the area is represented by a code sequence using the 8-neighbor window and the head area is detected analysing this code. The pixels in the head area is segmented by colors and the region most similar with the skin color is considered as a face area. The detected face area is represented by a rectangle including the area and its four vertices are used as the states of the Kalman estimator to trace the motion of the face area. It is proved by the experiments that the proposed method increases the accuracy of face detection and reduces the fare detection time significantly.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Korean Journal of Community Nutrition
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• v.23 no.3
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• pp.202-215
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• 2018

Objectives: We conducted a qualitative study to explore the feasibility of mobile applications for self-monitoring of diet. Methods: We conducted in-depth and focus group interviews with eight laymen who had used mobile dietary applications and eight experts. Interviews were audio-recorded and analyzed using an open coding method. Results: The qualitative data of our study revealed two key themes: (1) perceptions, opinions and attitudes towards mobile applications of self-monitoring of diet and (2) future directions to improve mobile applications. Conclusions: Our qualitative study suggested the potential use of mobile applications as a food-tracking and dietary monitoring tool and the need for improved mobile applications for self-monitoring of diet. The results of our study may provide insights into how to technically improve mobile applications for self-monitoring of diet, how to utilize dietary data generated through mobile applications, and how to improve individual's health though mobile applications.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.56-63
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• 2007

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and con-nected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based tex-ture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a 'aligns games.' The results show that the system process more than 30 frames/sec on PC for the $320{\times}240$ size input image and supply a user-friendly and convenient access to a computer in real-time operation.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.214-221
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• 2019

Visual perception information acquired through human eyes contains much information on how to view visual stimuli using eye tracking technology, it is possible to acquire and analyze consumer visual information as quantitative data. These measurements can be used to measure emotions that customers feel unconsciously, and they can be directly collected by numerically quantifying the character's search response through eye tracking. In this study, we traced the character's area of interest (AOI) and found that the average of fixation duration, count, average of visit duration, count, and finally the time to first fixation was analyzed. As a result of analysis, it was found that there were many cognitive processing processes on the face than the character's body, and the visual attention was high. The visual attention of attraction factor has also been able to verify that attraction is being presented as an important factor in determining preferences for characters. Based on the results of this study, further studies of more characters will be conducted and quantitative interpretation methods can be used as basic data for character development and factors to be considered in determining character design.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.270-277
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• 2008

The development of a face robot basically targets very natural human-robot interaction (HRI), especially emotional interaction. So does a face robot introduced in this paper, named Buddy. Since Buddy was developed for a mobile service robot, it doesn't have a living-being like face such as human's or animal's, but a typically robot-like face with hard skin, which maybe suitable for mass production. Besides, its structure and mechanism should be simple and its production cost also should be low enough. This paper introduces the mechanisms and functions of mobile face robot named Buddy which can take on natural and precise facial expressions and make dynamic gestures driven by one laptop PC. Buddy also can perform lip-sync, eye-contact, face-tracking for lifelike interaction. By adopting a customized emotional reaction decision model, Buddy can create own personality, emotion and motive using various sensor data input. Based on this model, Buddy can interact probably with users and perform real-time learning using personality factors. The interaction performance of Buddy is successfully demonstrated by experiments and simulations.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.141-150
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• 2010

The purpose of this study was to analyze the golf swing motion for a soft golf clubs and regular golf club. Soft golf is a newly developed recreational sports for all ages, including the elderly and the beginners of golf. To quantify the effect of using soft golf club, which much lighter club than regular clubs, a motion analysis has been performed using a 3D optoelectric motion tracking system that utilizes active infrared LEDs and near-infrared sensors. The subject performed swing motion using a regular golf club and a soft golf club in turn. The obtained motion capture data was used to build a 3D computer simulation model to obtain left wrist, elbow shoulder and lumbar joint force and torque using inverse and forward dynamics calculations. The joint force and torque during soft golf swing were lower than regular golf swing. The analysis gave better understanding of the effectiveness of the soft golf club.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.551-560
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• 2003

Particle deposition in human lungs was investigated theoretically by using asymmetric five-lobe lung model. The volumes of each of the five lobes were different, thereby forming an asymmetric lung structure. The tidal volume and flow rate of each lobe were scaled according to lobar volume. The total and regional deposition with various breathing patterns were calculated by means of tracking volume segments and accounting for particle loss during inhalation and exhalation. The deposition fractions were obtained for each airway generation and lung lobe, and dominant deposition mechanisms were investigated for different size particles. Results show that the tidal volume and flow rate have a characteristic influence on particle deposition. The total deposition fraction increases with an increase in tidal volume for all particle sizes. However, flow rate has dichotomous effects: a higher flow rate results in a sharp increase in deposition for large size particles, but decreases deposition for small size particles. Deposition distribution within the lung shifts proximally with higher flow rate whereas deposition peak shifts to the deeper lung region with larger tidal volume. Deposition fraction in each lobe was proportional to its volume. Among the three main deposition mechanisms, diffusion was dominant for particles < 0.5 ${\mu}{\textrm}{m}$ whereas sedimentation and impaction were most influential for larger size particles. Impaction was particularly dominant for particles> 8 ${\mu}{\textrm}{m}$. The results may prove to be useful for estimating deposition dose of inhaled pollutant particles at various breathing conditions.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.246-252
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• 2020

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.