• Journal of Korean Biological Nursing Science
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• v.2 no.2
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• pp.20-37
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• 2000

The purpose of this study was to investigate the relationships between the differentials in life styles and their effect on the epidermal facial tissue in order to provide a basis for health professionals so that they might better be able to maintain and promote healthy skin care and further delay the premature ageing of the epidermal facial tissue. The subjects consisted of 145 females of various ages who visited skin care room in cerming health promoting behaviors(Park In sook's Profile) and questions on their behavioral practices pertaining to personal skin care were used. The investigation also ess of the four parts of the epidermal facial tissue studied. All data collected wee entered into the SAS program and analyzed for frequency, percentages, the utilized Pusan. The study dates ranged from May 1, 1998 to May 30, 1998. The methods used for this investigation were a questionnaire survey consisting of general objective questions. The questions con a "skin analyzer" to measured levels of moisturizing hydrated, facial oils, and roughnmean, t-test, ANOVA, and Pearson Correlation Coefficients. The results of this study were as follows ; 1. Epidermal facial oil was at its highest levels in the chin area with additional decreasing levels in the forehead and nose regions. The least regions were those of the cheeks. The highest levels of hydration on the other hand started with the forehead followed by the area of the chin, the cheeks and the lowest level of epidermal facial hydration was in the region of the nose. 2. The average score of the performance in the health promoting behaviors variable was 139.51. The variables with the highest degree of the performance were rest and sleeping(35.71). The lowest degree was hiegenic life(23.44). 3. The relationship health promoting behaviors and epidermal condition was not correlated with oil, hydration and roughness of the skin surface. 4. Skin care behavioral characteristics related to epidermal condition were washing style and temperature of washing water. 5. General characteristics related to epidermal condition were occupation, education level, acne and melasma. In conclusion, this study showed that several factors were significant in the behavior of skin care. Clear knowledge of both internal and external factors which affect the epidermal condition will help women to pursue active and appropriate practices in their health behaviors and skin care.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.540-546
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• 2008

In the near future, robots should be able to understand human's emotional states and exhibit appropriate behaviors accordingly. In Human-Human Interaction, the 93% consist of the speaker's nonverbal communicative behavior. Bodily movements provide information of the quantity of emotion. Latest personal robots can interact with human using multi-modality such as facial expression, gesture, LED, sound, sensors and so on. However, a posture needs a position and an orientation only and in facial expression or gesture, movements are involved. Verbal, vocal, musical, color expressions need time information. Because synchronization among multi-modalities is a key problem, emotion expression needs a systematic approach. On the other hand, at low intensity of surprise, the face could be expressed but the gesture could not be expressed because a gesture is not linear. It is need to decide the emotional boundaries for effective robot behavior generation and synchronization with another expressible method. If it is so, how can we define emotional boundaries? And how can multi-modality be synchronized each other?

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.443-458
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• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• Proceedings of the SCSK Conference
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• 2003.09a
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• pp.387-412
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• 2003

Influence of gelatinase on basement membrane (BM) structure was investigated by using a skin equivalent (SE) model. The results showed that (1) gelatinase produced by cells degraded the BM and (2) the addition of matrix metalloproteinase-specific inhibitor to the SE medium accelerated the formation of BM structure, indicating that gelatinase is involved in BM impairment. The activity of gelatinase was also studied in healthy human facial skin tissues. The result of in situ zymography revealed gelatinase activity around the basal layer of the epidermis, where BM integrity was severely compromised. Therefore, this enzyme was suggested to be associated with BM decomposition in human facial skin. To assess the behavior of gelatinase in stratum corneum (SC) non-invasively, an immunological study was performed. Since positive immunostaining of pro-gelatinase B was observed in SC stripped from sun-exposed skin, whereas no positive staining detected in SC of non-irradiated skin, gelatinase in the epidermis could be non-invasively detected by measuring gelatinase in SC. Gelatinase in SC of healthy female volunteers was monitored using a special film that sensitively and conveniently detects gelatinase. Ninetr percent of SC from facial skin (l00 women, 40's-50's) was gelatinase-positive. On the other hand, SC from non-irradiated skin was negative. These results strongly suggest that (1) gelatinase is constantly produced in the facial epidermis of most middle-aged woman during their daily life, and (2) the enzyme might be involved in the aging-related degeneration of both BM and the matrix fibers of the upper layer of the dermis, acting as a very important aging factor. Strong inhibitory activity against gelatinase was found in turmeric extract and identified curcumin as the major ingredient. Topical application of cream containing turmeric extract significantly decreased the number of gelatinase-positive SC clusters in human facial skins. These results indicated that turmeric is an effective ingredient to prevent skin from photo aging by suppressing chlonically upregulated gelatinase activity by UV and to improve skin condition.

• Korean Journal of Community Nutrition
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• v.11 no.5
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• pp.587-597
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• 2006

This study was performed in order to identify nutritional factors affecting on skin sebum content with 131 female university undergraduates in 2003. We measured the sebum contents of 4 facial spots to classify their skin types. Daily energy and nutrient intakes of the subjects were not deficient except in calcium and iron, which were 466.2 mg (66.6% RDA) and 8.5 mg (53.4% RDA) relatively. We observed no significant difference of energy and nutrient intakes among the 3 skin types. But significantly higher consumption of grains and slightly higher frequencies of several food groups (excepting starches) were shown in oily skin types, so they might have higher nutrient intakes. Serum indices and food preferences mostly revealed no difference among the 3 skin types. But in the oily skin type, serum phosphorus levels were significantly lower than others, suggesting phosphorus-rich food consumption like soft drinks and pains could lead to a dry skin type rather than an oily one. Also in the oily skin type, sweet taste preference was slightly tower than others; more-over, sweet intake was lower samely significantly. There was mostly no significant relationship between facial sebum contents and nutrient intakes, dietary behavior, food frequency and food preference except in some factors. Animal protein intake showed a significant negative relationship toward facial sebum content. On the other hand, in normal skin type, Fishes consumed was slightly higher than others, so that higher animal protein consumption presumably leads to normal skin type. Frequency of fried food and bacon and preference of fried foods showed slightly negative relationships toward facial sebum content. Regular meal times showed significantly increased facial sebum content.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• 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 Digital Convergence
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• v.17 no.6
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• pp.433-438
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• 2019

This study investigated the effects of young children's emotional reading ability on prosocial behavior. The participants in this study were 192 young children's. From December 17, December 27, 2018, after conducting a survey on emotional reading ability and prosocial behavior of infants, the data was analyzed using the SPSS WIN 22.0 program for pearson correlation analysis and regression analysis. The results of the analysis suggest the following: First, there were significant relationships between young children's emotional reading ability and prosocial Behavior. Second, young children's emotional reading ability affected prosocial behavior. In conclusion, this study is believed to be the basis for the development of programs to improve emotional reading ability and promote prosocial behavior.

• Journal of Satellite, Information and Communications
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• v.11 no.1
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• pp.55-57
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• 2016

In this paper, we present an algorithm to determine the abnormal behavior through a CCTV-based behavioral recognition and a pattern of hand using ConvexHull. In the existing way that using CCTV for crime prevention, facial recognition is mainly used. Facial recognition is the way that compares the faces that are seen on the screen and faces of criminals for determining how dangerous targets are, however, this way is hard to predict future criminal behavior. Therefore, to predict more various situations, abnormal behaviours are determined with targets' incline of arms, legs and bodys and patterns of hand movements. it can forecast crimes when an acting has been getting within common normality out, comparing whose acting patterns with the crime patterns.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.