• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.142-147
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• 2012

Individuals with autistic spectrum disorders (ASD) have difficulty inferring other people's feelings from their facial expressions and/or from situational cues, and therefore, they are less able to respond with prosocial behavior. We developed a computer-based training program to help teach the connection between facial-based or situation-based emotions and prosocial behavioral responses. An 8-year-old male school child with ASD participated in the study. In this program, he was trained to identify persons in need of help and appropriate prosocial responses using novel photo-based scenarios. When he misidentified emotions from photographs of another's face, the program highlighted those parts of the face which effectively communicate emotion. To increase the likelihood that he would learn a generalized repertoire of emotional understanding, multiple examples of emotional expressions and situations were provided. When he misidentified persons expressing a need for help, or failed to identify appropriate helping behaviors, role playing was used to help him appreciate the state of mind of a person in need of help. The results of the training indicated increases in prosocial behaviors during a laboratory task that required collaborative work. His homeroom teacher, using a behavioral rating scale, reported that he now understood another's emotion or situation better than before training. These findings indicate the effects of the training are not limited to the artificial experiment situation, but also carried over to his school life.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.437-446
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• 2012

A face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into $5{\times}5$ blocks and depth values are averaged in each block. Then, $5{\times}5$ block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the $5{\times}5$ template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.161-168
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• 2021

Recently, many cognitive training programs based on digital contents have been presented, but few digital contents have been produced for children with intellectual disabilities among children with developmental disabilities. Digital contents based training programs are needed to apply remotely untact training programs rather than face-to-face apprenticeship classes for improving the cognition of children with intellectual disabilities. This study was attempted to present the new cognitive training program by analyzing concurrent validity, which was analyzed by the correlation between items by comparing with subtests of K-WISC-IV and items of game based Neuro-World cognitive training program. It was found that there are some validity by analyzing correlation between the subtests of K-WISC-IV and the items of Neuro-World program, which means that it has concurrent validity of some items of Neuro-World program. In terms of reliability, the Cronbach alpha value was 0.794, indicating that game performance of children with intellectual disabilities and the degree of level increase in each game were somewhat reliable. This is considered to be a significant result in future studies for clinical trials of cognitive training tools.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.489-494
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• 2015

Background: Once limited with face-to face courses, health education has now moved into the web environment after new developments in information technology This study was carried out in order to give training to the university academic and administrative female staff who have difficulty in attending health education planned for specific times and places. The web-supported training focuses on healthy diet, the importance of physical activity, damage of smoking and stress management. Materials and Methods: The study was carried out in Sakarya University between the years 2012-2013 as a descriptive and quasi experimental study. The sample consisted of 30 participants who agreed to take part in the survey, filled in the forms and completed the whole training. The data were collected via a "Personel Information Form", "Health Promotion Life-Style Profile (HPLSP)", and "Multiple Choice Questionnaire (MCQ). Results: There was a statistically significant difference between the total points from "Health Promotion Life-Style Profile" and the total points from the sub-scale after and before the training (t=3.63, p=0.001). When the points from the multiple choice questionnaire after and before training were compared, it was seen that the average points were higher after the training (t=8.57, p<0.001). Conclusions: It was found that web-supported health training has a positive effect on the healthy living behaviour of female staff working at a Turkish university and on their knowledge of health promotion.

• Journal of Korean society of Dental Hygiene
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• v.22 no.6
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• pp.551-561
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• 2022

Objectives: This study aimed to investigate the perceptions regarding oral muscle strength training for elder people among dental professionals. Methods: The study participants were selected using non-probability sampling methods, as dentists and dental hygienists with more than 3 years of work experience at dental institutions. A total of 15 participants were selected, including 6 dentists, 4 clinical dental hygienists, and 5 public dental hygienists. Interviews were conducted in June and July 2022, and two focus group interviews were conducted for each group. The first round was face-to-face and the second round was conducted through an online video conference. Results: Through focus group interviews, five factors were obtained; lack of awareness, value of training, factors necessary for implementation, performance status, and obstructive factors. It was found that most study participants had a negative perception regarding the application of oral muscle strength training for elder people due to the lack of information and education on the subject. However, the benefit of oral muscle strength training was positively recognized; adequate educational material, appropriate compensation, adequate time, and availability of patients will be necessary for proper training. Conclusions: It is necessary to improve the perceptions regarding the importance of strengthening oral muscles for the elder people among dentists and dental hygienists. In addition, high-quality educational material that can be easily comprehended and practiced should be developed for effective training.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.287-293
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• 2023

With the rapid development of domestic and international over-the-top markets, a large amount of video content is being created. As the volume of video content increases, consumers tend to increasingly check data concerning the videos before watching them. To address this demand, video summaries in the form of plot descriptions, thumbnails, posters, and other formats are provided to consumers. This study proposes an approach that automatically generates posters to effectively convey video content while reducing the cost of video summarization. In the automatic generation of posters, face recognition and clustering are used to gather and classify character data, and keyframes from the video are extracted to learn the overall atmosphere of the video. This study used the facial data of the characters and keyframes as training data and employed technologies such as DreamBooth, a text-to-image generation model, to automatically generate video posters. This process significantly reduces the time and cost of video-poster production.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10749-10755
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• 2015

Background: In both developed and developing countries; breast cancer is the major cancer observed in women. The aim of this study was to assess the effect of nursing and mammographic intervention on women with breast cancer between the ages of 50 and 70. Materials and Methods: A training program, which was quasi-experimental and had a pretest-protest design, was applied in Kemalpaaa district of Izmir, between October 2008 and August 2010. The target population was women between the ages of 50 and 70, who were registered in the list of 3rd Family Medicine Unit in Izmir's Kemalpasa metropolis. A total of 106 women who were in conformity with the study criteria participated in the study. Research data were collected through home visits that included face-to-face interviews; Ministry of Health education material and video films were modified and used for the training. Data analysis was performed through 82 women who were paired at the first and the second phase. Results: It was observed that although the rate of breast self examination significantly increased after the training (p=0.022), the rate of clinical breast examination (p=0.122) and mammographic screening (p=0.523) did not. Differences in the stages of change after training were found to be statistically significant (p<0.001) and the group showed a progression in the stages of change in general (46.3%). In women mean scores of breast cancer awareness (p<0.000), severity (p<0.000), health motivation (p<0.000) and perception of the benefits of mammography (p<0.000) increased significantly and mean score of perception of mammography barriers decreased significantly (p<0.000) after the training. Conclusions: After the training on breast cancer and mammography it was determined that nursing interventions provided positive progression of stages of change of women, affected health beliefs positively and significantly increased BSE incidences. However, it did not have a significant effect on CBE and mammographic screening.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3962-3980
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• 2019

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.22-30
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• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.99-108
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• 2021

There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.