Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

1  Introduction

Religious events involving the gathering of an enormous number of practitioners of the religion are significant ritualized features. A massive gathering event (MGE) can be no exception, an assembly of many people attending an organized event within a definite space [1]. There exist large public health consequences of MGE. Public health concerns during those events could encompass a range of health problems: spreading food, air, and water, exacerbating non-communicable diseases, vector-borne infectious disease, accidents, health consequences of alcohol and substance abuse, stampedes, and worsening of mental health problems [2]. The mass gathering is considered by the concentration of people at a particular place for a particular goal over a key planning recommendation for mass gathering during COVID19: Interim guidance-2-set time that can potentially strain the response and planning resources of the host community or country.

The mass gathering occurs as a single event or a grouping of numerous events at diverse places [3]. They might be private or public, spontaneous or planned, one-off or recurrent, and of different times and sizes. The range of mass gatherings is wider, from music, sports, religious, entertainment, or business events, to huge meetings and conferences. Some health interferences, like mass drug administrations or immunization campaigns, are considered mass gatherings [4]. The mass gathering includes high-visibility events, frequently related to international travel, great participation, extended media coverage, prolonged duration, and many venues (multiple host countries). Also, high-visibility events are often related to the improved frequency of small private gatherings (at home, in streets, restaurants or bars, etc.), which could characterize further difficulty since they are less controlled [5]. During the COVID19 pandemic, mass gatherings could be closely related to the high risk of spreading SARS-CoV-2; also, they can potentially strain the response and planning resources of the host community or country and be related to destructive effects on health services [6].

Organizing crowded event frequently includes complicated tasks, however, organizing them during pandemics or ongoing crises, namely COVID19 which needs adherence to numerous constraints, is increasingly sophisticated [7]. Also, many out-of-control crowded events exist with or without the participation of regulatory bodies (government of the region or country). Besides washing hands frequently and maintaining physical distancing, the appropriate usage of facemasks has now developed as the pillar to preventing community transmission of the disease [8]. The purpose is to protect oneself from getting infected and spreading the virus. Now, policymaker faces several risks and challenges while facing the transmission and spreading of COVID19. The laws and rules emerged as an action to considerably expand cases and deaths in several regions [9,10]. But monitoring massive groups of people has become increasingly complex.

This study introduces a new Deep Learning Based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in religious mass gatherings. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 model. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG model is examined on benchmark datasets.

2  Literature Review

In [11], a single shot multibox detector and MobileNetV2 (SSDMNV2) proposed for the detection of face masks utilizing MobileNetV2, TensorFlow, Keras, and OpenCV Deep Neural Network (DNN) structure are utilized as image classification. OpenCV DNN employed in SSDMNV2 has SSD, including ResNet-10, as the backbone and can identify faces from many angles. While MobileNetV2 offers lightweight and precise estimations for classification. In [12], a hybrid method utilizing deep and traditional ML for face mask detection is presented. This presented technique method has 2 elements, namely classification and feature extraction.

Gupta et al. [13] introduce a mask detector that utilizes an ML facial classification mechanism to determine whether the mask is worn. It is linked to a closed-circuit television (CCTV) mechanism verifying that only individuals wearing masks are allowable. Asif et al. [14] propose automatically utilizing DL to identify face masks in the video. The presented structure has 2 elements. Initially, it is devised to track and detect faces through ML and OpenCV; then, facial frames are processed into modelled deep transfer learning (DTL) MobileNetV2 to identify the mask region. In [15], an IoT-based smart door utilizes an ML technique for detecting face masks and monitoring body temperature. This method is utilized in apartment entrances, shopping malls, hotels, etc.

Loey et al. [16] intend to localize and annotate the face mask object in real-time images. Wearing a face mask in public places would protect individuals from the spread of transmission among them. The presented method has 2 elements. The first element was devised for the feature-extracting process related to the ResNet50 DTL method. At the same time, the second element was devised for detecting face masks related to YOLO v2. Singh et al. [17] suggest a method that would draw bounding boxes (green or red) around the faces of individuals, whether an individual was wearing a mask or not. The authors have also associated the performance of both methods: inference time and precision rate.

3  The Proposed Model

In this study, new DLFMD-RMG technology has been developed. The DLFMD-RMG technique focuses mainly on detecting face masks in religious mass gatherings. The DLFMD-RMG technique undergoes two pre-processing levels: the BF technique and Contrast Enhancement. Then, it involves two processes: face detection and face mask classification. Fig. 1 defines the overall flow of the DLFMD-RMG system.

Figure 1: Overall process of DLFMD-RMG approach

3.1 Face Detection Module

For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. YOLO is a novel target detection technique with higher accuracy and faster detection. The base YOLO model process image is at forty-five frames every second [18]. The YOLO network has 2 FC layers and twenty-four convolution layers. Interchanging 1 × 1 convolution layers reduces the feature space from previous layers. YOLO could pre-train the convolution layer on ImageNet classifier tasks at half the resolution (224 × 224 input image) and doubles the resolution for recognition. Several YOLO versions were generated. The YOLO v3 approach involves three techniques: YOLO v3-SPP, YOLO v3, and YOLO v3-tiny; the YOLO v4 approach involves four techniques: YOLO v4m-mish, YOLO v4s-mish, YOLO v4x-mish, and YOLO v4l-mish; YOLO v5 architecture involves YOLO v5s, YOLO v5n, YOLO v5x, and YOLO v5l.

The study chooses the YOLO v4s-mish, YOLO v5s, and YOLO v3 approaches for the study. The major reason is: firstly, the three modules could produce a test outcomes graph with a similar indicator that is appropriate for analysis and comparison; next, the three methods are light weighted, appropriate for target recognition in smaller scenes and smaller and medium datasets; Then, the 3 approaches depend on PyTorch DL architecture proposed by Facebook, and have better outcomes in target recognition.

The detection network is a convolution neural network (CNN) that encompasses output, transform, and convolution layers. The transform layer extracts activation of the convolution layer and increases the stability of the DNN. The location of pure bounding boxes can be generated using the output layer.

In this work, ResNet50 is employed as a deep transfer mechanism for extracting features [19]. A ResNet is a type of DTL that uses the residual network. ResNet50 has sixteen residual bottleneck blocks; every block has a convolutional size of 1×1, 3×3 and 1×1 with a feature map (64, 128, 256, 512, 1024). To calculate MSE loss among the target and predicted bounding boxes, the loss function of YOLO v5 is computed by:

YOLO v5 loss=Localization loss+Confidence loss+Classification loss (1)

The coefficient for computing the localization loss includes the grid cells’ height (h) and width (w), which is given below.

Localization loss=q1∑a=0g2⁡∑b=0v⁡1ijobj[(xj−x^)2+(yj−y^)2]+q1∑a=0g2⁡∑b=0v⁡1ijobj

[(wi−w^)2+(hi−h^i)2] (2)

From the expression, q1 refers to a weight, g indicates the number of grid cells, v denotes the number of bounding boxes in g, (xi, yi) represents a center of v in g, (wi,  hi) implies a width and height of v in g, (x^i, y^i) symbolizes a center of the target in every g, (w^i,  h^i) signify a center of the target in every g, 1ijobj means 1 when there is an object in v in every g or else 0.  Once the object is identified in v bounding boxes of g, Confidence loss calculates the confidence score of error. It is evaluated in the following expression.

Confidence loss=q2∑a=0g2⁡∑b=0v⁡1ijobj(csj−cs^i)2+q3∑i=0g2⁡∑j=0v⁡1ijnoobj(csj−cs^i)2 (3)

In Eq. (3), q2, q3 represents a weight of confidence error, csi shows the confidence score of v in g, 1ijobj is 1 when there is an object in v in every g or else 0, 1ijnoobj is 1 when there is no object in v in every g or else 0 is determined as follows.

Classification loss=q4∑a=0g2⁡1iobj∑z∈classes⁡(pi(z)−pi^(z))2 (4)

In Eq. (4), q4 indicates the weight of classifier error, pi(z) , and pi^(z) denotes the likelihood of the estimated object and actual conditional classes in a grid cell.

In addition, the face detection performance can be improved by the SOA for the hyperparameter tuning of the ResNet-50 model. The SOA performs extensive analysis of human search behaviours [20]. Through “experience gradient”, the search direction has been determined, and uncertain reasoning is used to solve the search step measurement.

The SOA has three major updating steps. Here, i refers to i - th individual searchers, and j characterizes the dimension of the individual. s shows the overall number of individuals; D represents the overall number of dimensions; t indicates the present algebra, and iter max signifies the maximal optimization algebra. xij(t) and xij(t+1) , correspondingly, epitomize the searcher site at t and (t+1) algebra.

The forward direction of a search can be determined using the experience gradient attained from the individual movement and the assessment of other individuals searching previous locations. The pre-emptive direction f→i,p(t) , egoistic direction f→i, e(t) , and altruistic direction f→i, a(t) of i - th individuals in other dimensions could be attained.

f→i,e(t)=p→i,best−x→i(t),

f→i,a(t)=g→i,best−x→i(t) ,  (5)

f→i,p(t)=x→i(t1)−x→i(t2).

The searcher uses a random weighted average to attain the search direction.

f→i(t)=sign(ωf→i,p(t)+ϕ1f→i,e(t)+ϕ2 f→i,a (t)),  (6)

In Eq. (6), t1, t2∈{t,  t−1,  t−2};  x→i(t1) and x→i(t2) denotes the best advantage of {x→i(t−2),  x→(t−1),  x→i(t)} ; gi, best refers to the previous optimum position in the locality where i - th search factors are positioned; pi, best denotes the optimal neighborhood from i - th search factors for the present locality; ψ1 and ɩψ1 indicate random numbers between zero and one; ω indicates the weight of inertia.

SOA describes the reasoning of fuzzy approximation capability. Through computer language, natural human language stimulates the behaviors of human intelligence. When the algorithm expressed fuzzy rules, it adapted to a better estimate of the optimization problem. But the smallest fitness corresponds to the smallest search step length.

μ(α)=e−α2/2δ2,  (7)

In Eq. (7), α and δ represent parameters of the membership function. The likelihood of the output parameter exceeding [−3δ, 3δ] is lesser than 0.0111. Then, μmin=0.011 . Usually, the optimum location of the individual has μ max =1.0 , and the worst location is 0.0111. Choose the following function as a fuzzy parameter with “smaller” target function values:

μi=μmax−s−Iis−I(μmax−μmin),  i=1, 2, …, s,   (8)

μij=rand (μi,  1) ,  j=1, 2,  …, D,  (9)

From the expression, μij can be defined using the above formula. Ii refers to the amount of the sequence X(t) of the present individual ordered from higher to lower values. The function rand (μi,  1) represents the real number within [μi,  1] . It is noted Eq. (8) simulate the random search behaviors of human. Step measurement of j‐th dimension search interspace is defined as follows.

αij=δij−−ln(μij),  (10)

In Eq. (10), δij denotes a variable of the Gaussian distribution function as follows.

ω=(iter max −t)itermax,   (11)

δij=ω|x→min−x→max|,  (12)

Now, ω indicates the weight of inertia. ω linearly reduced from 0.9 to 0.1 as the evolutionary algebra surges. x→min and x→max are the variates of minimal and maximal values.

After attaining the scout step measurement and direction, the position update is denoted as

xij(t+1)=xij(t)+αij(t)fij(t),  i=1, 2, …, s; j=1, 2, …,  D. (13)

fij(t) and αij(t) , correspondingly, the searcher search direction and step size at  t time.

The SOA algorithm derives a fitness function from accomplishing better classification performance. It describes a positive integer to characterize the improved candidate solution performance. The reduction of the classification error rate is regarded as the fitness function in the following.

fitness(xi)=ClassifierErrorRate(xi)=number of misclassified samplesTotal number of samples∗100 (14)

3.2 Face Mask Classification Model

In this study, the faces with and without masks are classified using the FNN model. NN is an effective and dynamic approach to understanding supervised learning [21]. The FNN includes output, input, rule, and membership function (MF) layers. Fig. 2 represents the framework of FNN. The dimensional of the input vector signifies the count of neurons from the input layer X=[x1, x2,  ..xn] . The MF layer calculates the membership degree of input modules. All the neurons in this layer define the linguistic variable. The MF Fij is determined as follows:

Fij=e(xi−cij)2σij2  (15)

Figure 2: Structure of FNN

The MF Fij illustrates the membership degree of input members ‘i’ of fuzzy set ‘j’. cij and σij2 define the center and width of membership Gaussian functions of xi . The Gaussian MFs are the base for linking betwixt fuzzy systems and RBFN. The outcome of this function has extremely smooth and continuously takes a maximal value of one. The multivariate Gaussian function was designed for the production of univariate sets. During the rule layer of FNN, all the neurons illustrate a fuzzy rule, and their resultant activation degree Aj , can be demonstrated as:

Aj=F1j(x1)F2j(x2)…Fnj(xn) (16)

A¯j=Aj∑j=1r⁡Aj  (17)

At this point, A¯j illustrates the normalization value, and r represents the count of neurons from the rule layer. The resultant layer identifies the outcome computation Y, which can be demonstrated as:

Y=∑j=1r⁡wjA¯j (18)

At this point, wj signifies the weighted connecting resultant and rule layers. During the fuzzy NN learning procedure, the main function is for developing and training the weighted connecting the resultant layer wj and rule layer, width σij2 and center cij of membership Gaussian function of xi. The gradient descent is the usual famous technique employed for enhancing these parameters. It can be an optimized technique employed for minimizing a function with iteratively subsequent the direction of steepest descents, as demonstrated by the gradients negative. It can be employed for updating the model parameter. Conversely, the DBN is BP-based fine-tuned, and FNN takes several benefits from supervised learning infrastructure. A most important benefit is that it improves the weight betwixt the rule and resultant layers by employing the BP technique one time, and the parameter present from the membership layer is also improved one time. Specifically, the BP technique in FNN could not be controlled in a repeating approach that effectively eliminates the gradient diffusion issue of DBN fine-tuned.

4  Results and Discussion

The face mask detection results of the DLFMD-RMG model are investigated using the dataset [22] comprising 1000 images described in Table 1. Fig. 3 illustrates some sample images with and without masks. The proposed model is simulated using Python 3.6.5 tool. The proposed model experiments on PC i5-8600k, GeForce 1050Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The parameter settings are learning rate: 0.01, dropout: 0.5, batch size: 5, epoch count: 50, and activation: ReLU.

Figure 3: Sample images

The confusion matrices accomplished by the DLFMD-RMG model on the identification of face masks on mass gathering in Fig. 4. The figures represented that the DLFMD-RMG model has categorized face mask images appropriately.

Figure 4: Confusion matrices of DLFMD-RMG approach (a) 80% of TR dataset, (b) 20% of TS data, (c) 70% of TR data, and (d) 30% of TS data

Table 2 offers face mask recognition outcomes of the DLFMD-RMG technique on 80% of training (TR) datasets and 20% testing (TS) data. Fig. 5 exhibits the quick face mask recognition results of the DLFMD-RMG system on 80% of the TR dataset. The DLFMD-RMG model has recognized images ‘with mask’ by accuy of 97.88%, precn of 97.97%, recal of 97.72%, Fscore of 97.85%, and MCC of 95.75%. Moreover, the DLFMD-RMG technique has identified images ‘without mask’ by accuy of 97.88%, precn of 97.78%, recal of 98.02%, Fscore of 97.90%, and MCC of 95.75%.

Figure 5: Face mask recognition result of DLFMD-RMG approach under 80% of TR dataset

Fig. 6 shows a brief face mask detection outcome of the DLFMD-RMG method on 20% of TS data. The DLFMD-RMG method has identified images ‘with mask’ by accuy of 98.50%, precn of 99.04%, recal of 98.10%, Fscore of 98.56%, and MCC of 97%. Furthermore, the DLFMD-RMG method has identified images ‘without mask’ by accuy of 98.50%, precn of 97.92%, recal of 98.95%, Fscore of 98.43%, and MCC of 97%.

Figure 6: Face mask recognition result of DLFMD-RMG approach under 20% of TS dataset

Table 3 offers face mask recognition outcomes of the DLFMD-RMG system on 70% of TR and 30% of TS datasets. Fig. 7 shows a brief face mask detection outcome of the DLFMD-RMG system on 70% of TR data. The DLFMD-RMG system has identified images ‘with mask’ by accuy of 95.86%, precn of 99.11%, recal of 92.80%, Fscore of 95.85%, and MCC of 91.92%. Furthermore, the DLFMD-RMG method has identified images ‘without a mask’ by accuy of 95.86%, precn of 92.82%, recal of 99.12%, Fscore of 95.86%, and MCC of 91.92%.

Figure 7: Face mask recognition result of DLFMD-RMG approach under 70% of TR dataset

Fig. 8 demonstrates a brief face mask detection outcome of the DLFMD-RMG system on 30% of TS data. The DLFMD-RMG method has identified images ‘with mask’ by accuy of 96.33%, precn of 97.76%, recal of 94.24%, Fscore of 95.97%, and MCC of 92.66%. Furthermore, the DLFMD-RMG technique has identified images ‘without mask’ by accuy of 96.33%, precn of 95.18%, recal of 98.14%, Fscore of 96.64%, and MCC of 92.66%.

Figure 8: Face mask recognition result of DLFMD-RMG approach under 30% of TS dataset

A brief ROC investigation of the DLFMD-RMG method under the test database is illustrated in Fig. 9. The results indicated the DLFMD-RMG system had shown its capacity to classify different classes.

Figure 9: ROC curve analysis of the DLFMD-RMG model

Eventually, a comparative study of the DLFMD-RMG with recent approaches is given in Table 4 and Fig. 10 [12,16,23]. These results inferred that the DLFMD-RMG model had shown enhanced outcomes over other models. For instance, based on accuy , the DLFMD-RMG model has attained an increased accuy of 98.50%. Besides, based on precn , the DLFMD-RMG model has achieved an improved precn of 98.48%. Also, based on the recal , the DLFMD-RMG model has accomplished an improved recal of 98.52%. Therefore, the DLFMD-RMG model can be employed for face mask recognition in religious mass gatherings.

Figure 10: Comparative analysis of DLFMD-RMG system with recent methodologies

5  Conclusion

In this study, new DLFMD-RMG technology has been developed. The DLFMD-RMG technique focuses mainly on detecting face masks in religious mass gatherings. To accomplish this, the presented DLFMD-RMG technique undergoes two levels of pre-processing: BF technique and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the SOA for tuning the hyperparameter of the ResNet-50 model. At last, the faces with and without masks are classified using the FNN model. The experimental validation of the DLFMD-RMG algorithm is examined on a benchmark dataset, and the results highlight the promising performance of the DLFMD-RMG technique over other recent approaches. Ensemble deep learning models can be employed for improved face mask recognition results. Besides, a hybrid metaheuristic algorithm can be designed to enhance classification performance.

Funding Statement: This work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under grant no. (HO: 023-611-1443). The authors, therefore, gratefully acknowledge DSR technical and financial support.

Conflicts of Interest: The authors declare they have no conflicts of interest to report regarding the present study.

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