Masked Face Recognition Model with Explainable AI

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person's physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory rate, and forehead temperature for people wearing masks due to the pandemic. Using the green (G) minus red (R) signal in the RGB image, the region of interest (ROI) is established in the forehead and nose bridge regions. The photoplethysmography (PPG) waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method, baseline drift calibration, normalization, and bandpass filtering. The relevant parameters in Deep Neural Networks (DNN) for the regression model can correctly predict the heartbeat and blood pressure. In addition, the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths. Meanwhile, the thermal image can be used to read the temperature average of the ROI of the forehead, and the forehead temperature can be obtained smoothly. The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3% and the error of forehead temperature within ±0.5°C. © 2023 Tech Science Press. All rights reserved.

Region-of-Interest Aware 3D ResNet for Classification of COVID-19 Chest Computerised Tomography Scans

Coronavirus disease 2019, commonly known as COVID-19, is an extremely contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computerised Tomography (CT) scans based diagnosis and progression analysis of COVID-19 have recently received academic interest. Most algorithms include two-stage analysis where a slice-level analysis is followed by the patient-level analysis. However, such an analysis requires labels for individual slices in the training data. In this paper, we propose a single-stage 3D approach that does not require slice-wise labels. Our proposed method comprises volumetric data pre-processing and 3D ResNet transfer learning. The pre-processing includes pulmonary segmentation to identify the regions of interest, volume resampling and a novel approach for extracting salient slices. This is followed by proposing a region-of-interest aware 3D ResNet for feature learning. The backbone networks utilised in this study include 3D ResNet-18, 3D ResNet-50 and 3D ResNet-101. Our proposed method employing 3D ResNet-101 has outperformed the existing methods by yielding an overall accuracy of 90%. The sensitivity for correctly predicting COVID-19, Community Acquired Pneumonia (CAP) and Normal class labels in the dataset is 88.2%, 96.4% and 96.1%, respectively. © 2013 IEEE.

Deep Crowd Analysis to Spot Social Distancing Violations in Post-COVID 19 Lifestyle

Crowd analysis is a new field of study that involves processing a large group of people to examine one or more of their behaviors. Deep learning is an appropriate technique for crowd analysis using a convolutional neural network. To calculate the distance between crowd members and to identify social distance violations, a deep crowd analysis is proposed in this study. Pre-trained in a single class To discover the region of interest, CNN is utilised to classify people (RoI). The people in the picture are then localized using a density map. The reference point used to calculate the distance between the people is the centroid of the isolated areas in the density map. A social distance violation is reported if the estimated distance is less than the specified threshold distance (3 meters). Between the two ROIs, a distance measured in pixels is determined. © 2022 IEEE.

Edge-enhanced instance segmentation by grid regions of interest.

This paper focuses on the instance segmentation task. The purpose of instance segmentation is to jointly detect, classify and segment individual instances in images, so it is used to solve a large number of industrial tasks such as novel coronavirus diagnosis and autonomous driving. However, it is not easy for instance models to achieve good results in terms of both efficiency of prediction classes and segmentation results of instance edges. We propose a single-stage instance segmentation model EEMask (edge-enhanced mask), which generates grid ROIs (regions of interest) instead of proposal boxes. EEMask divides the image uniformly according to the grid and then calculates the relevance between the grids based on the distance and grayscale values. Finally, EEMask uses the grid relevance to generate grid ROIs and grid classes. In addition, we design an edge-enhanced layer, which enhances the model's ability to perceive instance edges by increasing the number of channels with higher contrast at the instance edges. There is not any additional convolutional layer overhead, so the whole process is efficient. We evaluate EEMask on a public benchmark. On average, EEMask is 17.8% faster than BlendMask with the same training schedule. EEMask achieves a mask AP score of 39.9 on the MS COCO dataset, which outperforms Mask RCNN by 7.5% and BlendMask by 3.9%.

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Navigating the cellular landscape in tissue: Recent advances in defining the pathogenesis of human disease.

Over the past decade, our understanding of human diseases has rapidly grown from the rise of single-cell spatial biology. While conventional tissue imaging has focused on visualizing morphological features, the development of multiplex tissue imaging from fluorescence-based methods to DNA- and mass cytometry-based methods has allowed visualization of over 60 markers on a single tissue section. The advancement of spatial biology with a single-cell resolution has enabled the visualization of cell-cell interactions and the tissue microenvironment, a crucial part to understanding the mechanisms underlying pathogenesis. Alongside the development of extensive marker panels which can distinguish distinct cell phenotypes, multiplex tissue imaging has facilitated the analysis of high dimensional data to identify novel biomarkers and therapeutic targets, while considering the spatial context of the cellular environment. This mini-review provides an overview of the recent advancements in multiplex imaging technologies and examines how these methods have been used in exploring pathogenesis and biomarker discovery in cancer, autoimmune and infectious diseases.

Remote Pulse Estimation in the Presence of Face Masks

Remote photoplethysmography (rPPG), a family of techniques for monitoring blood volume changes, may be especially useful for contactless health monitoring via face videos from consumer-grade cameras. The COVID-19 pandemic caused widespread use of protective face masks, which results in a domain shift from the typical region of interest. In this paper we show that augmenting unmasked face videos by adding patterned synthetic face masks forces the deep learning-based rPPG model to attend to the periocular and forehead regions, improving performance and closing the gap between masked and unmasked pulse estimation. This paper offers several novel contributions: (a) deep learning-based method designed for remote photoplethysmography in a presence of face masks, (b) new dataset acquired from 54 masked subjects with recordings of their face and ground-truth pulse waveforms, (c) data augmentation method to add a synthetic mask to a face video, and (d) evaluations of handcrafted algorithms and two 3D convolutional neural network-based architectures trained on videos of unmasked faces and with masks synthetically added. © 2022 IEEE.

Artificial intelligence enabled non-invasive T-ray imaging technique for early detection of coronavirus infected patients.

A new artificial intelligence (AI) supported T-Ray imaging system designed and implemented for non-invasive and non-ionizing screening for coronavirus-affected patients. The new system has the potential to replace the standard conventional X-Ray based imaging modality of virus detection. This research article reports the development of solid state room temperature terahertz source for thermograph study. Exposure time and radiation energy are optimized through several real-time experiments. During its incubation period, Coronavirus stays within the cell of the upper respiratory tract and its presence often causes an increased level of blood supply to the virus-affected cells/inter-cellular region that results in a localized increase of water content in those cells & tissues in comparison to its neighbouring normal cells. Under THz-radiation exposure, the incident energy gets absorbed more in virus-affected cells/inter-cellular region and gets heated; thus, the sharp temperature gradient is observed in the corresponding thermograph study. Additionally, structural changes in virus-affected zones make a significant contribution in getting better contrast in thermographs. Considering the effectiveness of the Artificial Intelligence (AI) analysis tool in various medical diagnoses, the authors have employed an explainable AI-assisted methodology to correctly identify and mark the affected pulmonary region for the developed imaging technique and thus validate the model. This AI-enabled non-ionizing THz-thermography method is expected to address the voids in early COVID diagnosis, at the onset of infection.

Detection of Face Mask Using Convolutional Neural Network

According to data acquired by the World Health Organization, the worldwide universal of COVID-19 bears harshly hit the realm and bears immediately contaminate eight heaps of human beings in general. Wearing face masks and following cautious public leave behind are two of the embellished protection from harm rules of conduct that need to take the place of honestly held places in consideration of keeping from happening or continuing the spread of the virus. To develop in mind or physically conservative surroundings that contribute to public protection from harm, we suggest an adept data processing machine located in close contact with the genuine in existence-period made or done by a human being to discover two reliable public dissociate themselves and face masks honestly placed by the model ahead of the start of the model to monitor special interests or pursuits and discover rape through photographic equipment. In addition to presenting an alarm to the public, in this proposed structure, we have designed mask detection along which indicates people to wear their mask properly before permitting in to the area which they prefer. We have used machine learning with supports the accuracy for the prediction. © 2022 IEEE.

Deep Learning-Based Face Mask Detecting System: An Initiative Against COVID-19

In this era of pandemic, wearing mask and taking precaution is a must, and this paper provides a way of detecting mask with the help of deep learning. Deep learning is an important part of machine learning. OpenCV, an important part of Python, is used for real-time image detection. This is a very decent system which can be applied at various platforms and can help in slowing down the transmission of coronavirus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Social Distancing Crowd Segmentation, Estimation and Visualisation

This research shows a modern crowd counting solution which alters typical prediction solutions into a segmentation of individuals based on a distance threshold, allowing for better visualisation and results. The study proposes using YOLOv4-normal and YOLOv4-tiny models, which have shown great results throughout calibration with an MAE of 14 and 36 respectively. However it did present some issues of accuracy degradation when trained on head annotations at any level of crowd density. As for visualisation, perspective transformation was used which directly helped in providing the distance calculation that was absent from standard transformation. If any variants of YOLOv4 are to be used, the main argument is the choice between speed over accuracy while relying on native implementations. In the case of distance regulation, any transformation that maps itself onto the region of interest, such as perspective transformation should be used to precisely determine distances from a camera to the region of interest itself. © 2021 IEEE.

A Grad-CAM and YOLO based approach to extracting a human passageway ROI

We propose a computer vision based solution for the use of public video feeds to monitor crowd congestion with a focus on full automation as a potential scalable solution to address crowd statistics extraction needs amplified by the COVID-19 pandemic. The novelty is the provision of a fully autonomous solution that is able to generate a region of interest (ROI) upon initial feed registration with a self-refinement algorithm that perfects the ROI over time. Five classes were used from the Places 2 dataset. The root model of the hierarchy was used to classify between a beach, fast-food restaurant, train station, lawn and market with an overall accuracy of 95.58% and F1-Score of 88.94%. The market and beach class were then split into two sub-classes each. The 'beach' model was further explored using a Grad-CAM based post-processing technique to better understand what the model bases the classification on. The novelty is the use of the same technique to generate a human passageway region of interest based on the localisation of the Grad-CAM in several live beach footages. These were also inferred using a YOLOv5 based human tracking approach. The Grad-CAM based ROI was then evaluated for each footage on the YOLOv5 generated ROI. © 2021 IEEE.

Contactless Palm Vein Biometric Authentication and Health Monitoring Device

Today, the novel COVID-19 pandemic has taken a significant toll on countries worldwide and impacted the lives and well-being of people across nations. Measures need to be taken to slow down the spread of such viruses, which can be reduced by taking proper precautions to avoid unnecessary contact and incurring hygienic habits. People can become infected by touching infected objects or surfaces, then touching their eyes, nose or mouth. We need proper and hygienic authentication systems for granting access to authorized users at various places such as companies, universities, banks, etc. Today there are multiple biometric systems that can serve this purpose, but to maintain hygiene we need systems that are contactless in nature thus to reduce spread of infections through touch. The palm vein pattern is distinctive biometric identity of individuals that is also a safe and reliable biometric authentication technique. The major advantage of palm vein biometric that we consider in our proposed system is that this kind of authentication can be done in a contactless way. Here, we propose an authentication system that uses palm vein pattern biometric to authenticate users in a contactless way. We have also added the functionalities of temperature detection and blood oxygen level detection to this system. These health symptoms are the major symptoms of various fatal virus infections such as the coronavirus. By checking the users for these symptoms before granting access, we can further limit the spread of infections and also help detect the infected patients. The proposed system does this in a contactless way. © 2021 IEEE.

Particle Swarm Optimization for Adaptive Social-distance of Neighborhood in the IoT and COVID-19 Era

While it is well understood that the emerging Social Internet of Things (SIoT) offers a description of a new world of billions of humans which are intelligently communicate and interact with each other. SIoT presents new challenges for suggesting useful objects with certain services for people. This is due to the limitation of social networks between human and objects, such as the evaluation of the various patterns inherent in human walk in cities. In this study we focus services on the problem of recommendation on SIoT which is very important for many applications such as urban computing, smart cities, and health care. The optimized results of swarm of certain infected people COViD-19 introduced in this paper aims at finding a given region of interest. Guided by a fitness function, the particle swarm optimization (PSO) algorithm has proved its efficiency to explore the search space and find the optimal solution. However, in real world scenarios in which the peoples are simulated as particles, there are practical constraints that should be taken into considerations. The most two significant constraints are (1) given the social-distance, the measurement of input variable fluctuations and their possibility of occurring via probability distribution function over the whole particles. (2) given the limited the communication range of particle/people/users, therefore, the spread of the diseases are simulated and evaluated using neighborhood particle swarm optimization (NPSO). © 2021 IEEE.

One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

We present a model that fuses lesion segmentation with Attention Mechanism to predict COVID-19 from chest CT scans. The model segments lesions, extracts Regions of Interest from scans and applies Attention to them to determine the most relevant ones for image classification. Additionally, we augment the model with Long-Short Term Memory Network layers that learn features from a sequence of Regions of Interest before computing attention. The model is trained in one shot for both problems, using two different sets of data. We achieve 0.4683 mean average precision for lesion segmentation, 95.74% COVID- 19 sensitivity and 98.15% class-adjusted F1 score for image classification on a large CNCB-NCOV dataset. Source code is available on https://github.com/AlexTS1980/COVID-LSTM-Attention. IEEE