• Proceedings of the Korean Society of Computer Information Conference
• /
• 2009.01a
• /
• pp.429-432
• /
• 2009

최근 현금인출기의 설치가 증가하면서 그에 따른 범죄도 증가하는 추세이다. 현금인출기와 관련한 지능형 범죄가 증가하고 있지만 보안 시스템은 CCTV나 감시카메라를 이용하고 있어 취약한 면이 많다. 특히 감시 카메라는 실제 상황을 단순하게 녹화하는 가능만을 제공하기 때문에 모자나 마스크, 선글라스 등으로 안면을 가리고 현금인출기를 이용하면 범죄 사건 발생 후에 범인의 신원을 확인할 수 없는 문제점이 발생한다 따라서 본 논문에서는 효과적인 현금인출기 보안 시스템을 위한 안면 인식 알고리즘을 제안한다. 이 알고리즘은 현금인출기의 감시 카메라를 이용하여 사용자의 안면을 인식하고, 그 영상에서 장신구 착용 유무 판별함으로써 정상식인 얼굴과 비정상적인 얼굴을 구별한다. 성능평가에서는 정상적인 얼굴, 선글라스 착용 상태, 마스크 착용 상태, 선글라스와 마스크를 모두 작용한 상태에 대하여 안면 인식률을 비교 평가한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.4
• /
• pp.89-96
• /
• 2021

The number of confirmed cases of coronavirus-19 is soaring around the world and has caused numerous deaths. Wearing a mask is very important to prevent infection. Incidents and accidents have occurred due to the recommendation to wear a mask in public places such as buses and subways, and it has emerged as a serious social problem. To solve this problem, this paper proposes an open source-based face mask wearing monitoring system. We used open source software, web-based artificial intelligence tool teachable machine and open source hardware Arduino. It judges whether the mask is worn, and performs commands such as guidance messages and alarms. The learning parameters of the teachable machine were learned with the optimal values of 50 learning times, 32 batch sizes, and 0.001 learning rate, resulting in an accuracy of 1 and a learning error of 0.003. We designed and implemented a mask wearing monitoring system that can perform commands such as guidance messages and alarms by determining whether to wear a mask using a web-based artificial intelligence tool teachable machine and Arduino to prove its validity.

• Journal of Digital Convergence
• /
• v.16 no.12
• /
• pp.571-577
• /
• 2018

Bicycle riders complain of many inconveniences when wearing glasses, sunglasses and masks. The disposable fine dust mask has been developed to resolve such inconveniences of bicycle riders. The scope of research is limited to the fiber which generates one-time static electricity and the shape of the mask due to the characteristic of fine dust mask. The purpose is to design and make the fine dust mask with a simple production process. The new disposable fine dust mask has secured enough space of the mask in front of the mouth by longitudinal folding and minimized stuffiness by maintaining the shape of the folded part to prevent touching the mouth even by heavy breathing. The streamlined sponge is attached at the part of nose support and the area of the cheek has been expanded to be about 2.5cm wider than ordinary masks to improve tight seal at the side. In addition, a new disposable fine dust scarf mask has been developed to block ultraviolet rays for the face and neck while filtering fine dust with the tight fine dust mask.

• Journal of the Korean Crystal Growth and Crystal Technology
• /
• v.27 no.4
• /
• pp.191-195
• /
• 2017

The microstructures of nonwoven fabric with and without nanofibers used as a filter for air purification type mask were investigated using scanning electron microscope. Moreover, we proposed a new mask design which is effective in the improvement of the fine dust blocking property. When comparing to nonwoven fabrics of which an average diameter was $25{\mu}m$, the nanofibers formed by the electrospinning process had a tight mesh structure arranged irregularly with a relatively large specific surface area, which could be associated with their much smaller diameter ranging from 25 to 120 nm. Such a prominent structural feature at nanofibers led to mechanical adsorption of fine particles, resulting in enhancement of filtering behavior maintaining high permeability. In addition to the excellent performance of the mask filter, wearing the mask properly is expected to maximize the blocking property of fine dust. To meet such a requirement, a new mask design that can be closely attached to the face in order to effectively block fine dust entering the gap between the face and the mask.

• Journal of the Korean Applied Science and Technology
• /
• v.38 no.2
• /
• pp.465-475
• /
• 2021

This study conducted an online and offline survey of 210 people from March 11 to 27, 2021 for the purpose of investigating and analyzing the current status of skin stress recognition and beauty care behavior due to wearing masks. The collected data were analyzed using SPSS 25.0 with Cronbach's α, Frequency Analysis, Chi-square test, and One way Anova. The average daily mask wearing time of more than 7 hours during the Covid-19 period was 43.8%, and skin stress recognition by wearing masks was highest among those in their 30s (M=4.27) and service workers (M=4.64), and those with acne and skin troubles (M=4.47) perceived high stress. The most important factor for home care treatment was cleansing(67.6%) and for beauty care was skin care(36.7%). Considerations factors on beauty care were 54.3% for service and customer care capabilities, and on body shape management method 45.7% for exercise. According to this study, respondents are recognizing skin stress due to the long-term use of masks, and home care treatment has been increasing as the esthetic salon has become unstable to visit due to the Covid-19 epidemic.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.171-187
• /
• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• 월간산업보건
• /
• s.147
• /
• pp.52-53
• /
• 2000

• Clean Technology
• /
• v.4 no.1
• /
• pp.60-67
• /
• 1998

As workers' health and welfare are becoming increasingly important, there are needs for protecting workers from being exposed to dust particles in workplace. One of the simplest ways to protect workers is to wear a one-time-use mask of the 3M type. Although conventional masks are convenient to use, they provide low filtration efficiency simply because of air leaks through interface between the face and the mask. In this study, a new type of mask is developed and tested. The air which is filtered first using high efficiency particulate air(HEPA) filter is supplied to the nose area of workers' face. The developed mask was tested for filtration efficiency and for workers' acceptance. Very good results were obtained for both accounts.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.285-286
• /
• 2020

전 세계적으로 유행하는 COVID-19(코로나19)로 인해 사람들은 대면 접촉을 피하게 되었고, 전염성이 높은 이유로 마스크의 착용이 의무화되고 있고, 이를 검사하는 업무가 증가하고 있다. 그래서, 인공지능 기술을 통해 업무를 도와줄 수 있는 출입자 통계와 출입자 마스크 착용 검사를 할 수 있는 시스템이 필요하다. 이를 위해 본 논문에서는 딥러닝 알고리즘을 활용한 출입자 통계와 마스크 착용 판별 시스템을 제시한다. 또한, 실시간 영상인식에 많이 활용되고 있는 YOLO-v3와 YOLO-v4, YOLO-Tiny 알고리즘을 데스크탑 PC와 Nvidia사의 Jetson Nano에 적용하여 알고리즘별 성능 비교를 통해 적합한 방법을 찾고 적용하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.04a
• /
• pp.373-376
• /
• 2010

요즘 현금카드나 신용카드를 훔치고 비밀번호를 알아내 ATM(현금자동지급기)에서 현금을 인출하는 범죄가 늘고 있는데 범인들은 대부분 선글라스, 안경, 마스크, 모자 등으로 얼굴을 가리고 인출을 함으로 은행의 CCTV는 범인색출에 거의 도움이 되지 않는다. 본 논문에서의 영상처리는 모두 Java언어를 사용하였으며 피부색을 사전 추출하는 과정을 거쳐 구현된 분류기능을 이용해 얼굴의 이목구비들의 위치를 인식하도록 한다. 이는 ATM이용자가 선글라스, 안경, 마스크 등으로 얼굴을 가리면 기기에서 애초에 서비스 받는 것을 불가능 하게 하여, 범죄를 예방할 수 있게 한다. 또한 카드의 사용자 정보와 서비스를 시도했던 시간과 캡쳐 이미지를 저장해 놓음으로써 범인의 인상착의, 알리바이 등을 확인하는데 크게 도움을 주는 ATM 안면 인식 보안 시스템의 가능성을 제안하고자 한다.