• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.647-650
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• 2021

The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.294-301
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• 2022

COVID-19 is a crisis with numerous casualties. The World Health Organization (WHO) has declared the use of masks as an essential safety measure during the COVID-19 pandemic. Therefore, whether or not to wear a mask is an important issue when entering and exiting public places and institutions. However, this makes face recognition a very difficult task because certain parts of the face are hidden. As a result, face identification and identity verification in the access system became difficult. In this paper, we propose a system that can detect masked face using transfer learning of Yolov5s and recognize the user using transfer learning of Facenet. Transfer learning preforms by changing the learning rate, epoch, and batch size, their results are evaluated, and the best model is selected as representative model. It has been confirmed that the proposed model is good at detecting masked face and masked face recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1136-1141
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• 2022

Researches of face recognition on masked faces have been increasingly important due to the COVID-19 pandemic. To realize a stable and practical recognition performance, large amount of facial image data should be acquired for the purpose of training. However, it is difficult for the researchers to obtain masked face images for each human subject. This paper proposes a novel method to synthesize a face image and a virtual mask pattern. In this method, a pair of masked face image and unmasked face image, that are from a single human subject, is fed into a convolutional autoencoder as training data. This allows learning the geometric relationship between face and mask. In the inference step, for a unseen face image, the learned convolutional autoencoder generates a synthetic face image with a mask pattern. The proposed method is able to rapidly generate realistic masked face images. Also, it could be practical when compared to methods which rely on facial feature point detection.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.944-947
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• 2022

Due to the coronavirus pandemic, the wearing of a mask has been increasing worldwide; thus, the importance of image analysis on masked face images has become essential. Although head pose estimation can be applied to various face-related applications including driver attention, face frontalization, and gaze detection, few studies have been conducted to address the performance degradation caused by masked faces. This study proposes a new data augmentation that synthesizes the masked face, depending on the face image size and poses, which shows robust performance on BIWI benchmark dataset regardless of mask-wearing. Since the proposed scheme is not limited to the specific model, it can be utilized in various head pose estimation models.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.319-331
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• 2022

The latest global COVID-19 pandemic has made the use of facial masks an important aspect of our lives. People are advised to cover their faces in public spaces to discourage illness from spreading. Using these face masks posed a significant concern about the exactness of the face identification method used to search and unlock telephones at the school/office. Many companies have already built the requisite data in-house to incorporate such a scheme, using face recognition as an authentication. Unfortunately, veiled faces hinder the detection and acknowledgment of these facial identity schemes and seek to invalidate the internal data collection. Biometric systems that use the face as authentication cause problems with detection or recognition (face or persons). In this research, a novel model has been developed to detect and recognize faces and persons for authentication using scale invariant features (SIFT) for the whole segmented face with an efficient local binary texture features (DLBP) in region of eyes in the masked face. The Fuzzy C means is utilized to segment the image. These mixed features are trained significantly in a convolution neural network (CNN) model. The main advantage of this model is that can detect and recognizing faces by assigning weights to the selected features aimed to grant or provoke permissions with high accuracy.

• (The) Research of the performance art and culture
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• no.20
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• pp.213-249
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• 2010

Aside from the Kangryung masked dance, a masque dance in the near of Ongjin county in Hwanghae province has been transmitted. This article was written by two researchers to investigate this fact in a new way. Inwoo Song had conducted a focus interview aimed at persons who came from Ongjin to South Korea and Hyungho Chung had been responsible for summarizing and analyzing the interview. Bonyoung and Suyoung were a place in which a navy admiral stayed and a very rich area. These two places and Kangryung invited and perfomed each other. They kept a competition relationship for the performance. For this reason, the masque dance had advanced in this area. Compared to Kangryung masked dance, this masked dance had a difference in the performance order including a lion dance. Malddukyi dance was expressed in detail in the first part and Hanryang dance was performed independently in the middle of the dance. Sangjwa dance among dances was considered important. Also a lively Malddukyi dance was a scale which measured the skill of the dance. Chwiballyi starred in the dance. The mask was made from a paper and had a grotesque ghost face. This dance was played around Dan-oh. The music accompaniment was conducted by professional musician group, "Kyungjanpae", who lived in the vicinity. They had talents such as masked dance, tightrope dancing, tumbling in addition to music accompaniment. Especially, a local shaman took part in the Bonyoung masked dance and leaded an excorcism after the death of Halmi. Also, in the part of the Kwangdaedaegam gut, the shaman and village people put on the mask and danced together. Therefore, a shaman was closely connected with a local masked dance. After the Korean war, Kangryung masked dance was restored but Bongyoung and Sogang masked dance was not transmitted at all. This article will be helpful for completing the script and restoring the Bongyoung and Sogang masked dance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.139-146
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• 2008

Masked fake face detection using ordinary visible images is a formidable task when the mask is accurately made with special makeup. Considering recent advances in special makeup technology, a reliable solution to detect masked fake faces is essential to the development of a complete face recognition system. This research proposes a method for masked fake face detection that exploits reflectance disparity due to object material and its surface color. First, we have shown that measuring of albedo can be simplified to radiance measurement when a practical face recognition system is deployed under the user-cooperative environment. This enables us to obtain albedo just by grey values in the image captured. Second, we have found that 850nm infrared light is effective to discriminate between facial skin and mask material using reflectance disparity. On the other hand, 650nm visible light is known to be suitable for distinguishing different facial skin colors between ethnic groups. We use a 2D vector consisting of radiance measurements under 850nm and 659nm illumination as a feature vector. Facial skin and mask material show linearly separable distributions in the feature space. By employing FIB, we have achieved 97.8% accuracy in fake face detection. Our method is applicable to faces of different skin colors, and can be easily implemented into commercial face recognition systems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.23-26
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• 2020

본 논문에서는 CCTV를 통해 얻은 영상에서 얼굴을 인식하여 자동으로 출석 여부를 체크하는 시스템을 소개한다. 이 시스템은 CNN을 바탕으로 RetinaFace 모델을 사용하여 얼굴을 탐지하고, 탐지된 얼굴을 ArcFace 모델로 R⁵¹²의 목표 공간으로 임베딩한다. 기존 데이터베이스에 등록된 얼굴과 CCTV를 통해 얻은 얼굴들의 임베딩 벡터 사이의 Angular Cosine Distance를 측정하여 동일 인물인지 판단하는 매칭 알고리즘을 제안한다. 실험을 통해 두 모델을 동시에 사용할 최적의 환경을 파악하고, 마스크 착용으로 얼굴의 하단부가 가려지는 폐색 문제에 더욱 효과적으로 대응하여 매칭 성능을 높이는 방법을 제안한다.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.208-214
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• 2021

Since face masks in public were mandated during COVID-19, more people have taken temperature checks, with their masks on. The study has developed a contactless thermal camera that accurately measures temperatures of people wearing different kinds of masks, detect people wearing masks wrong, and record the temperature data. The built-in system that identifies people wearing masks wrong is what masks our contactless thermal camera differentiated from other thermal cameras. Also our contactless thermal camera can keep track of the number of mask wearers in different regions and their temperatures. Thus, the analysis of such regional data can significantly contribute to stemming the spread of the virus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.