• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.9
• /
• pp.1120-1131
• /
• 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 Korean society of Dental Hygiene
• /
• v.11 no.5
• /
• pp.811-822
• /
• 2011

Objectives : The purpose of this study was to examine the relationship of the self-efficacy of dental hygiene students to their stressors and ways of stress-adaptation patterns. Methods : The subjects in this study were dental hygiene juniors in four selected colleges located in the metropolitan area. Results : 1. They got a mean of 3.22 in self-efficacy. They gave the highest mark(3.50) to an item 'I can attain it if I set a primary goal.' 2. They got a mean of 3.18 in stressors. Among the stressors, task assignments(3.74) were identified as the greatest stressor, followed by the curriculum(3.25), learning environments(3.16), prospects of employment (3.07) and test anxiety(2.95). 3. They got a mean of 2.02 in stress-adaptation method. They got 2.31 and 1.72 in long-term and short-term adaptation respectively, which showed that long-term stress adaptation method were more prevailing than short-term ones. 4. As a result of analyzing whether there would be any gaps in self-efficacy according to general characteristics, statistically significant gaps were found in that regard according to experience of preparing for college admission after leaving high school, academic standing, satisfaction with the department of dental hygiene and prospects of employment(p<.05). 5. As a result of checking the relationship of their self-efficacy to their stressors and ways of stress adaptation method, there were statistically significant differences in that aspect according to examinations and prospect of employment(p<0.05). As for ways of stress adaptation method, there were statistically significant differences in long-term adaptation method (p<0.05). Conclusions : As it's found that the level of the self-efficacy of the dental hygiene students was linked to the efficiency of their ways of stress adaptation method, the development and implementation of programs geared toward boosting the self-efficacy of dental hygiene students are required to teach them to successfully cope with various kinds of stress that they are likely to face after getting a job.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.3
• /
• pp.241-246
• /
• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.461-469
• /
• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• Journal of the Korean Society of Radiology
• /
• v.18 no.2
• /
• pp.101-108
• /
• 2024

Recently, Metaverse technology has emerged as an important topic in various fields. Metaverse refers to a three-dimensional virtual space in which social and economic activities similar to the real world are possible. Among the 235 third-year students who applied the Metaversity app in the radiology department of this university from September to December 2023, 200 participated in a survey to determine the difference in student response and satisfaction when applying the Metaversity app. analyzed. First, the most satisfactory VOD viewing method was viewing through the Metaversity app, followed by viewing through the LMS. Second, 'I think online videos are appropriate for holiday reinforcement.' showed the highest score at 4.35±0.60, 'I want face-to-face classes and online classes to be held simultaneously.' was 4.25±0.87, and 'I think meta. 'I watched it well through the Metaversity app' was the lowest at 4.10±0.30, and 'VOD viewing through the Metaversity app was used appropriately in class' was the lowest at 3.99±0.75. Also, there was no significant difference in the response to the teaching method (p>0.05). Third, in terms of satisfaction with VOD viewing using the Metaversity app, 'Applying the Metaversity app was interesting and fun' ranked the highest at 4.24±0.88. The score was high, with 'Better improvement is needed to actively utilize the metaversity app' at 4.00±0.45, and 'I hope the metaversity app is implemented in other remote classes' at 3.77±0.88. appear. 'VOD classes through the Metaversity app are better than the existing LMS method.' was found to be 3.44±0.66. Additionally, there was no significant difference in satisfaction with classes according to age and gender (p>0.05). The correlation between response and satisfaction with the metaversity app is 0.601, which can be considered very significant (p>0.001). As a limitation of this study, although we surveyed students' satisfaction with using the Metaversity app, we were unable to investigate the satisfaction of instructors who interact with students. In the future, we did not consider the instructor's satisfaction in classes using the Metaversity app. Research must be conducted, and universities must have institutional support and continued interest until metaversity apps are selected and used to prepare for distance learning.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.2
• /
• pp.89-103
• /
• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• Korean Journal of Cognitive Science
• /
• v.35 no.1
• /
• pp.49-95
• /
• 2024

Polygraph examination, statement validity analysis and P300-based concealed information test are major three examination tools, which are use to determine a person's truthfulness and credibility in criminal procedure. Although polygraph examination is most common in criminal procedure, but it has little admissibility of evidence due to the weakness of scientific basis. In 1990s to support the weakness of scientific basis about polygraph, Farwell and Donchin proposed the P300-based concealed information test technique. The P300-based concealed information test has two strong points. First, the P300-based concealed information test is easy to conduct with polygraph. Second, the P300-based concealed information test has plentiful scientific basis. Nevertheless, the utilization of P300-based concealed information test is infrequent, because of the quantity of probe stimulus. The probe stimulus contains closed information that is relevant to the crime or other investigated situation. In tradition P300-based concealed information test protocol, three or more probe stimuli are necessarily needed. But it is hard to acquire three or more probe stimuli, because most of the crime relevant information is opened in investigative situation. In addition, P300-based concealed information test uses oddball paradigm, and oddball paradigm makes imbalance between the number of probe and irrelevant stimulus. Thus, there is a possibility that the unbalanced number of probe and irrelevant stimulus caused systematic underestimation of P300 amplitude of irrelevant stimuli. To overcome the these two limitation of P300-based concealed information test, one-probe P300-based concealed information test protocol is explored with various machine learning algorithms. According to this study, parameters of the modified one-probe protocol are as follows. In the condition of female and male face stimuli, the duration of stimuli are encouraged 400ms, the repetition of stimuli are encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. In the condition of two-syllable word stimulus, the duration of stimulus is encouraged 300ms, the repetition of stimulus is encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. It was also conformed that the logistic regression (LR), linear discriminant analysis (LDA), K Neighbors (KNN) algorithms were probable methods for analysis of P300 amplitude. The one-probe P300-based concealed information test with machine learning protocol is helpful to increase utilization of P300-based concealed information test, and supports to determine a person's truthfulness and credibility with the polygraph examination in criminal procedure.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.6
• /
• pp.175-182
• /
• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.317-326
• /
• 2024

Biometric recognition is a technology that determines whether a person is identified by extracting information on a person's biometric and behavioral characteristics with a specific device. Cyber threats such as forgery, duplication, and hacking of biometric characteristics are increasing in the field of biometrics. In response, the security system is strengthened and complex, and it is becoming difficult for individuals to use. To this end, multiple biometric models are being studied. Existing studies have suggested feature fusion methods, but comparisons between feature fusion methods are insufficient. Therefore, in this paper, we compared and evaluated the fusion method of multiple biometric models using fingerprint, face, and iris images. VGG-16, ResNet-50, EfficientNet-B1, EfficientNet-B4, EfficientNet-B7, and Inception-v3 were used for feature extraction, and the fusion methods of 'Sensor-Level', 'Feature-Level', 'Score-Level', and 'Rank-Level' were compared and evaluated for feature fusion. As a result of the comparative evaluation, the EfficientNet-B7 model showed 98.51% accuracy and high stability in the 'Feature-Level' fusion method. However, because the EfficietnNet-B7 model is large in size, model lightweight studies are needed for biocharacteristic fusion.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.