Deep Learning Framework to Detect Face Masks from Video Footage

The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.Comment: Contains 6 pages and 6 figures. Published in 12th CICN 202

Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning

       في عصر جائحة مرض كوفيد-19, لعبت أدوات هندسة البرمجيات والذكاء الاصطناعي دورا رئيسيا في مراقبة انتشارالفيروس وإدارته والتنبؤبه.وبحسب التقاريرالصادرةعن منظمة الصحة العالمية والتي توصي بجميع محاولات الوقاية من أي شكل من أشكال العدوى بين الناس، وخاصة في الأماكن العامة. احدى هذه المحاولات في تجنب العدوى هومطالبة الناس بارتداء أقنعة الوجه. على اية حال ،ولأسباب شخصية،لايميل بعض الأشخاص إلى ارتداء أقنعة الوجه لغرض الوقاية. الهدف من هذه الورقة العلمية هو بناء اداة برمجية تدعى كشف قناع الوجه لاكتشاف وتحديد اي شخص لايرتدي قناع الوجه وخاصة في الاماكن العامة باستخدام كاميرات المراقبة. التقنية لهذه الفكرة هي استخدام عدد كبير من صور وجوه الأشخاص, حيث ان بعض الصورلوجوه مرتدية اقنعة والبعض الاخر لايرتدي أقنعة. طريقة الكشف هي باستخدام تعليم الالة بواسطة الرسم البياني للمشتقات الموجهة لاستخراج العناصر المهمة, وتميزيها باستخدام آلة المتجهات الداعمة وهذه الطريقة تساهم بكشل كبير بتكامل وتحسين عملية كشف الاقنعة. عدة قواعد بيانات تحتوي على صور الوجوه المرتدية اقنعة متوفرة للعام وقد تم استخدامهم في تجارب هذا البحث. والنتيجة كانت كالاتي : 97%, 100%, 97.5%, 95% لRWMFD  & GENLI4k , SMFDB,MFRD, و MAFA & GENKI4k  بالتتابع. من خلال مقارنة نتائج نسب التمييز لهذا البحث مع بحوث في نفس التخصص وكانت النتائج واعدة ومنافسة. الجدير بالذكر ان تنفيذ هذا العمل تم باستخدام حاسوب شخصي بواسطة برنامج الماتلاب وكاميرة لفحص العمل في الوقت الحقيقي.       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific public area by using CCTV (closed-circuit television). The problem also occurs in case the software tool is inaccurate. The technique of this notion is to use large data of face images, some faces are wearing masks, and others are not wearing masks. The methodology is by using machine learning, which is characterized by a HOG (histogram orientation gradient) for extraction of features, then an SVM(support vector machine) for classification, as it can contribute to the literature and enhance mask detection accuracy. Several public datasets for masked and unmasked face images have been used in the experiments. The findings for accuracy are as follows: 97.00%, 100.0%, 97.50%, 95.0% for RWMFD (Real-world Masked Face Dataset)& GENK14k, SMFDB (Simulated Masked Face Recognition Dataset), MFRD (Masked Face Recognition Dataset), and MAFA (MAsked FAces)& GENK14k for databases, respectively. The results are promising as a comparison of this work has been made with the state-of-the-art. The workstation of this research used a webcam programmed by Matlab for real-time testing

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed

An Accurate Real-Time Method for Face Mask Detection using CNN and SVM

Infectious respiratory diseases, including COVID-19, pose a significant challenge to humanity and a potential threat to life due to their severity and rapid spread. Using a surgical mask is among the most significant safety precautions that can help keep this sort of pandemic from spreading, and manual monitoring of large crowds in public places for face masks is problematic. In this research, we suggest a real-time approach for face mask detection. First, we use a multi-scale deep neural network to extract features. As a result, the attributes are better suited for training the detection system. We employ SVM post-processing in the classification stage to make the face mask detection method more robust. According to the experimental findings, our strategy considerably decreased the percentage of false positives and undetected cases

The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.Comment: Accepted at BIOSIG202

Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR