Robust Face Mask Detection with Combined Frontal and Angled Viewed
Faces
Ivan George L. Tarun
1
, Vidal Wyatt M. Lopez
1
, Patricia Angela R. Abu
1
and Ma. Regina Justina E. Estuar
2
1
Ateneo Laboratory for Intelligent Visual Environment, Dept. of Information Systems and Computer Science,
Ateneo De Manila University, Katipunan Avenue, Quezon City, Philippines
2
Ateneo Center for Computing Competency and Research,Ateneo De Manila University,
Katipunan Avenue, Quezon City, Philippines
Keywords:
Face Mask Detection, Object Detection, Deep Learning, Computer Vision.
Abstract:
One such protocol currently enforced by the Philippine government to combat COVID-19 is the mandatory
use of face masks in public places. The problem however is that ensuring people follow this protocol is
difficult to monitor during a pandemic due to other conflicting health protocols like social distancing and
workforce reduction. This study therefore explores on the creation of deep learning models that consider both
frontal and side view images of the face for face mask detection. In doing so, improvements to robustness
were found when compared to using models that were previously trained on purely frontal images. This
was accomplished by first relabeling a subset of images from the FMLD dataset. These images were then
split into train, validation, and test sets. Four deep learning models (YOLOv5 Small, YOLOv5 Medium,
CenterNet Resnet50 V1 FPN 512x512, CenterNet HourGlass104 512x512) were later trained on the training
set of images. These four models were compared with three models (MobileNetV1, ResNet50, VGG16) that
were trained previously on purely frontal images. Results show that the four models trained on the relabeled
FMLD dataset offer an approximately 20% increase in classification accuracy over the three models that were
previously trained on purely frontal images.
1 INTRODUCTION
New infectious diseases continue to emerge to this
day with them contributing to the already long list of
discovered illnesses. With that said, emerging infec-
tions (EIs) are infectious diseases that have just re-
cently appeared, or they have already existed but only
now are they quickly expanding in geographic range
or occurrence (Petersen et al., 2018). There has been a
constant fight to contain EIs throughout history due to
their global burden of being among the leading causes
of death and disability.
Moreover, infectious diseases have continued to
evolve throughout history with different diseases hav-
ing various potential to spread globally (Fauci, 2001).
COVID-19 is one recent disease which is caused by
the zoonotic virus known as the Severe Acute Res-
piratory Syndrome Coronavirus 2 or SARS-CoV-2
(C¸ elik et al., 2020). It is classified as a highly trans-
missible and pathogenic viral infection. COVID-19
first emerged in Wuhan, China at the end of 2019 and
subsequently escalated into a global pandemic. Upon
its first emergence, the novel coronavirus managed to
kill more than 1,800 people and infect over 70,000 in-
dividuals in the first 50 days of the epidemic in China
(Shereen et al., 2020). With this as context, the Philip-
pines as of the beginning of February 2022, has sur-
passed 3.6 million total infections along with 3.5 mil-
lion recoveries while 54,000 have died (Department
of Health (Philippines), 2022).
One protocol currently enforced by the Philippine
government to combat COVID-19 is the mandatory
use of face masks in public places (Lazaro et al.,
2020). The problem however is that ensuring peo-
ple follow this protocol during a pandemic is difficult
to monitor due to conflicting health protocols (social
distancing & workforce reduction). Compliance also
tends to lessen over time due to complacency (Choud-
hary et al., 2021). To help solve this, the Ateneo Lab-
oratory for Intelligent Visual Environment (ALIVE)
created a real-time face mask detection model for
video feeds (Lopez et al., 2021). It checks if a per-
son is wearing a mask or not, and if so, checks if the
mask is medically approved or not. Still, the model is
462
Tarun, I., Lopez, V., Abu, P. and Estuar, M.
Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces.
DOI: 10.5220/0010986000003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 462-470
ISBN: 978-989-758-569-2; ISSN: 2184-4992
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

limited by only being trained to detect face masks of
those who look directly into the camera.
Given the limitations of the front-facing face mask
detection model, this study aims to improve on the
robustness of the computer vision models in (Lopez
et al., 2021) by training new models that also consider
angled or side view images of the face.
2 REVIEW OF RELATED
LITERATURE
2.1 COVID-19 Health Protocols
From an international perspective, the World Health
Organization (WHO) published a document titled
“Mask use in the context of COVID-19: interim guid-
ance” (World Health Organization, 2020). To sum-
marize, the WHO recommends the general popula-
tion to wear at least fabric masks in public settings
where physical distancing of at least one meter can-
not be maintained and ventilation is known to be poor.
Meanwhile, only people with an increased risk of se-
vere complications from contracting COVID-19 are
recommended to wear medical masks. These peo-
ple include those who are aged 60 and above along
with those who have underlying comorbidities. As
for those who are suspected or confirmed to have
COVID-19, they should always wear a medical mask
no matter the community setting.
Transitioning to a more localized perspective, the
“Omnibus Guidelines on the Implementation of Com-
munity Quarantine in the Philippines Updated as of
September 23, 2021” dictates comprehensive proto-
cols on how the COVID-19 pandemic should be man-
aged within the Philippines as prepared by the Inter-
Agency Task Force for the Management of Emerging
Infectious Diseases (Inter-Agency Task Force for the
Management of Emerging Infectious Diseases, 2021).
Mask use is also recommended by these guidelines,
medical or non-medical, similar to that of the WHO.
However, there are some key differences particularly
that wearing of masks are mandatory in any setting
(indoor or outdoor) outside one’s own residence.
Overall, these protocols provide deeper context re-
garding the problem of this study which is the moni-
toring of the compliance of the public to COVID-19
health protocols. Also note that the technical details
of these protocols vary per organization and that there
is no universal answer regarding which is the cor-
rect one to follow. Multiple organizations nonetheless
agree that the wearing of face masks is recommended
in combating COVID-19. This therefore solidifies the
reasoning behind this study which is to create a com-
puter vision model that monitors the wearing of face
masks.
2.2 Previous Attempts in Creating a
Computer Vision Model for
Detecting Face Masks
A study created a novel deep learning model for
face mask detection (Loey et al., 2021). ResNet-50
(Residual Neural Network) with transfer learning and
YOLO v2 (You Only Look Once) was used for fea-
ture extraction and detection of face masks respec-
tively in the training, validation, and testing phases.
The YOLO family of detectors were used for reasons
of speed and performance. Mean Intersection over
Union (IoU) was also used in the study to estimate
the best number of anchor boxes and increase model
performance. Data augmentation was also performed
to increase the diversity of their dataset by flipping
images horizontally. The study then compared two
optimizer techniques used in improving detector per-
formance, Stochastic Gradient Descent with Momen-
tum (SGDM) and the Adam optimizer. Their results
show that SGDM was better than Adam in training
time, validation Root Mean Square Error (RMSE),
and validation Loss. However, Adam was better in
Mini-batch RMSE and Loss. Evaluation of the detec-
tor performance resulted in Adam being better than
SGDM with an average precision (AP) of 0.81 while
SGDM had 0.61 in all recall levels. Miss rates were
also lower and better with Adam with it having a
log-average miss rate of 0.4 while SGDM had 0.6.
The proposed detector model was then compared with
other related works wherein it was found that it per-
formed the best even if AP only reached 81%.
Another study implemented a real-time face mask
detection system for embedded systems (Lopez et al.,
2021). This study comes from the aforementioned
ALIVE laboratory which the current paper seeks to
improve upon. The face mask detection system aims
to do three class classification namely for wearing
medically approved masks, non-medically approved
masks, and wearing no mask. Medically approved
masks include surgical masks and N95 respirators
while non-medically approved masks consist of body
parts covering the face (e.g., hands), scarfs, and
cloth face masks. A comparative analysis was sub-
sequently conducted between MobileNetV1, VGG16,
and ResNet50 to determine which deep learning
model to use for the face mask detection system. In
doing so, the MAsked FAces (MAFA) dataset (Ge
et al., 2017) was manually reclassified to fit the three
aforementioned mask wearing types. The three mod-
Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces
463

els were then trained on this subdataset, also incorpo-
rating data augmentation, for 20 epochs each with a
batch size of 32 and a learning rate of 1e-4. Results
show that the MobileNetV1 achieved the highest vali-
dation accuracy of 79% followed by VGG16 (76%)
and then ResNet50 (37%). After this, the models
were ran on a Raspberry Pi 4 Model B (4GB RAM)
embedded system and classified video captured from
a webcam to see their real-time performance in terms
of frames per second (fps). MobileNetV1 had the
highest fps with 9.6, then ResNet50 (5.13 fps), and
finally VGG16 (4.62 fps).
2.3 Mask Detection Datasets
The aforementioned MAFA dataset is one potential
dataset that can be used (Ge et al., 2017). It is meant
for masked face detection and consists of 30,811 im-
ages containing 35,806 masked human faces. Each
masked face has six main attributes which are the
locations of the face, eyes, masks, face orientation,
occlusion degree, and mask type. Of interest to the
current study is the face orientation attribute wherein
five orientations were defined specifically left, front,
right, left-front, and right-front. Next, the mask type
attribute consists of four categories including Simple
Mask (pure color), Complex Mask (complex textures
or logos), Human Body (face covered by hand, hair,
etc.) and Hybrid Mask (combinations of at least two
mask types, or one mask type with eyes occluded by
glasses).
One criticism of the MAFA dataset is that it is
more suited for occlusion detection rather than mask
detection (Nowrin et al., 2021). To solve this issue
amongst others, a study created the Face Mask Label
Dataset (FMLD) (Batagelj et al., 2021). It is a com-
bination of the MAFA and WIDER FACE datasets
(Yang et al., 2016). FMLD overall has 41,934 im-
ages containing 63,072 faces labeled as either cor-
rectly masked, incorrectly masked, or unmasked. Ad-
ditional annotations are also available like gender,
pose, and ethnicity.
2.4 Deep Learning Models
In order to determine the best deep learning models to
use for the face mask detection, multiple architectures
were benchmarked in this study which is further dis-
cussed in Section 3. Some of the benchmarked mod-
els include the CenterNet architecture (Zhou et al.,
2019) which involves anchorless object detection. It
replaces the traditional Non-Maximum Suppression
(NMS) with a simpler, faster, and more accurate al-
gorithm. This different approach models objects as a
single point which is the center point of the bound-
ing box. Keypoint estimation is then used to find
the center points while regression is used for find-
ing other object properties such as size, location, and
pose. CenterNet operates on the insight that relevant
box predictions can be determined depending on the
location of their centers instead of their overlap with
the object as in NMS. Less garbage predictions are
therefore made compared to anchor-based detection
along with removing the need for the computationally
heavy NMS.
The rest of the benchmarked models incorporate
the YOLOv5 architecture (Jocher et al., 2021). In
general, YOLO works by dividing an image into a
system of grids where each grid detects objects within
itself. This system results in consuming less compu-
tational resources during operation. The main differ-
ences of YOLOv5 to its predecessors start with its
backbone. YOLOv5 combines Cross Stage Partial
Networks or CSPNet (Wang et al., 2020) with Dark-
net to form CSPDarknet as its backbone to extract
features from an input image. CSPNet solves the du-
plicate gradient problems usually found in large-scale
backbones and therefore leads to fewer parameters
and floating-point operations per second. These fur-
ther yield faster inference speed and reduced model
size. As for the model neck, YOLOv5 utilizes the
Path Aggregation Network or PANet (Liu et al., 2018)
to generate feature pyramids that aggregate and pass
features to the model head. PANet introduces a new
feature pyramid structure which improves the propa-
gation of low-level features by having an enhanced
bottom-up path. All-in-all, PANet contributes bet-
ter location accuracy for objects. The model head
remains the same between YOLOv5 and YOLOv4
which is responsible for producing class predictions
and bounding boxes. It is capable of multi-scale
detection or handling small, medium, and large ob-
jects (Xu et al., 2021). Other differences present in
YOLOv5 include the use of the Leaky Rectified Lin-
ear Unit and Sigmoid activation functions to over-
come the vanishing gradient problem. The loss func-
tion has also switched to the Binary Cross-Entropy
with Logits Loss function. These last two differences
help YOLOv5 to learn faster and perform better.
3 METHODOLOGY
This section contains the necessary steps that were
performed for this study namely Dataset Relabeling,
Splitting the Dataset, Model Training, and Model
Comparison. These are further elaborated in their re-
spective subsections.
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3.1 Dataset Relabeling
With the goal of this study to improve the models
in (Lopez et al., 2021), the dataset required needs
to be similar wherein there are classes for Medically
Approved Masks, Non-Medically Approved Masks,
and No Masks. These would be for the frontal view
of the face. Another set of the three classes would
be needed for the side or angled views of the face.
Taking this into consideration, either the MAFA or
FMLD datasets need to be relabeled in order to have
the same set of classes in (Lopez et al., 2021).
Therefore, Dataset Relabeling was done based on
the FMLD dataset. The makesense.ai tool (Skalski,
2019) was used to relabel a subset of images into
six classes namely Front - Medical Mask, Front -
Non Medical Mask, Front - No Mask, Side - Medi-
cal Mask, Side - Non Medical Mask, and Side - No
Mask. Frontal images are defined as faces where all
its parts are in view (both eyes, whole nose, and whole
mouth). Side view images then are defined as either
purely left or purely right images of the face with only
some parts of the face in view (one eye, half nose,
half mouth, one ear). Medical Masks include surgical
masks and N95 respirators while Non Medical Masks
consist of scarfs, cloth face masks, and construction
masks. Sample images can be seen in Figure 1.
Figure 1: Sample images from the relabeled FMLD dataset.
3.2 Splitting the Dataset
Moving forward, the relabeled FMLD dataset was
split into train, validation, and test sets. Eighty per-
cent (80%) of the images were used as the train set,
ten percent (10%) as the validation set, and ten per-
cent (10%) as the test set. Care was taken to ensure
that there was an equal number of images per class
in each respective set. This was done through the
train test split function with the stratify parameter of
the scikit-learn package in Python (Pedregosa et al.,
2011).
3.3 Model Training
After dataset splitting, four deep learning models
were trained on the training set. Two of them are
the YOLOv5 Small and YOLOv5 Medium models
(Jocher et al., 2021). The other two are the Center-
Net Resnet50 V1 FPN 512x512 and CenterNet Hour-
Glass104 512x512 models from the Tensorflow 2 Ob-
ject Detection API Model Zoo (Huang et al., 2017).
Transfer learning was done with the four models pre-
trained on the COCO 2017 dataset. Each model also
comes with their own standard set of data augmenta-
tions which were left as is. Next, the four models were
each trained for 300 epochs. The YOLOv5 Small and
Medium models had a batch size of 32 and 16 respec-
tively with both having an input image size of 640
pixels. The CenterNet Resnet50 V1 FPN 512x512
and CenterNet HourGlass104 512x512 models then
had a batch size of 16 and 4 respectively with both
having an input image size of 512x512 pixels.
YOLOv5 models were chosen for their focus on
speed while the two CenterNet models were cho-
sen for their focus on performance. Mean Aver-
age Precision (mAP) at an Intersection over Union
(IoU) of 0.5 to 0.95, or simply mAP@IoU=0.5:0.95
was then recorded during training using the valida-
tion set. The same goes for mAP@IoU=0.5. Total
loss was also recorded for the training and validation
sets. Training was done on a local desktop computer
with an Nvidia GTX 1080 Ti GPU. The best and last
weights of each model were saved afterwards. The
best weights were chosen based on the highest value
of mAP@IoU=0.5:0.95 across the 300 epochs.
3.4 Model Comparison
Following Model Training, the four models were
compared with the three deep learning models (Mo-
bileNetV1, ResNet50, VGG16) from (Lopez et al.,
2021) to see if training with side view images of
the face would improve face mask detection from the
sides. This was done by comparing the accuracy of
the models in classifying the six classes of the re-
labeled FMLD dataset. Classification accuracy was
chosen as the comparison metric because the three
models from (Lopez et al., 2021) are classification
models while the four models of this study are ob-
ject detection models. Classification accuracy would
be a common metric that can be measured for both
types of models. One issue in measuring the classi-
fication accuracy for the three models from (Lopez
et al., 2021) was that they were only trained to clas-
sify three classes (Medical Mask, Non Medical Mask,
No Mask) while the relabeled FMLD dataset has six
Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces
465

classes as seen in Section 3.1. The eventual solution
was to consider the predictions of the three models as
correct or wrong depending on if they were able to
predict the mask type of a face correctly no matter if
the face had a frontal or side view. So for example, if
the VGG16 model gave a prediction of Medical Mask
and the ground truth label was Side - Medical Mask,
then this was considered as correct.
The validation and test sets were then combined
next in preparation for measuring the classification
accuracy of all the seven models. This was done to
increase the number of images per class so that clas-
sifying a single image correctly or wrongly would not
affect the overall classification accuracy too much. In-
ference was then done with each of the seven mod-
els on the combined validation and test set wherein
the class with the highest confidence score from the
prediction was chosen as the predicted class for the
particular image. Accuracy per class was calculated
afterwards by dividing the number of correct predic-
tions over the total number of images per class and
then multiplying by 100 to get a percentage value.
Calculating the classification accuracies along with
Inferencing was done with Python. The best weights
of the four models of this study were also used for
Inferencing. The PyTorch package (Paszke et al.,
2019) was used for Inferencing the YOLOv5 models
while the Tensorflow package (TensorFlow Develop-
ers, 2021) was used for Inferencing the two CenterNet
models.
4 RESULTS AND DISCUSSIONS
4.1 Relabeled FMLD Dataset
The completion of the dataset relabeling step pro-
duced a subdataset of images from the FMLD dataset
that was subsequently used for this study. The rela-
beled FMLD dataset therefore consists of 300 images
in total with 50 images per class. Each image only
contains one face so that the dataset can be easily bal-
anced during splitting. A breakdown of the relabeled
FMLD dataset can be seen in Table 1.
4.2 Dataset Splits
Splitting the relabeled FMLD dataset resulted in three
image sets. The training set thus contains 240 im-
ages or 80% of the total number of images. Equal
distribution of the 240 images leads to 40 images per
class. The validation set then consists of 30 images
or 10% of the total images. This resolves to 5 images
per class. Lastly, the test set also includes 30 images
Table 1: Breakdown of the relabeled FMLD dataset.
Class Number of Samples
Front - Medical Mask 50
Front - Non Medical Mask 50
Front - No Mask 50
Side - Medical Mask 50
Side - Non Medical Mask 50
Side - No Mask 50
Total 300
and also represents 10% of the total images. This too
then gives 5 images per class.
4.3 Model Training
Figure 2 depicts the graphs of the
mAP@IoU=0.5:0.95 and mAP@IoU=0.5 values
for the four models of this study during training.
It can be seen that the two CenterNet models have
higher mAP values than the YOLOv5 models and
they plateau earlier as well while the YOLOv5 mod-
els have a more gradual increase. Table 2 provides
a summary of the mAP values of the four models
wherein the best and last values are displayed along
with the epoch where they occur. The highest value
of mAP@IoU=0.5:0.95 (0.761451) was achieved by
the CenterNet Resnet50 V1 FPN 512x512 model at
Epoch 184. The YOLOv5 models have a lower value
ranging from 0.05 to 0.08 to the CenterNet models.
As for mAP@IoU=0.5, the highest value (0.994499)
was achieved by the CenterNet HourGlass104
512x512 model at Epoch 33. The YOLOv5 models
also have a lower value with a range of 0.01 to 0.02
to the CenterNet models.
Table 2: Summary of Mean Average Precision values.
mAP @ IoU = 0.5:0.95 mAP @ IoU = 0.5
Model Best Last Best Last
YOLOv5 Small
0.701899
Epoch 258
0.641859
Epoch 300
0.980495
Epoch 257
0.921729
Epoch 300
YOLOv5 Medium
0.681231
Epoch 300
0.681231
Epoch 300
0.974667
Epoch 284
0.896467
Epoch 300
CenterNet Resnet50
V1 FPN 512x512
0.761451
Epoch 184
0.725012
Epoch 300
0.99057
Epoch 124
0.97454
Epoch 300
CenterNet HourGlass104
512x512
0.758944
Epoch 112
0.707889
Epoch 300
0.994499
Epoch 33
0.912096
Epoch 300
Bold = Highest Value
The total loss curves of the YOLOv5 and Center-
Net models are shown in Figures 3 and 4 respectively.
It is evident that the CenterNet models were over-
fitting during training as the validation total losses
started to fluctuate at around Epoch 30. These may
also explain why the best values for mAP of the Cen-
terNet models occur at the earlier epochs and not near
the end. The YOLOv5 models on the other hand had
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Figure 2: Mean Average Precision graphs of the four mod-
els of this study while training.
their validation total losses decreasing up to the very
end of the 300 epochs and were not overfitting. Thus,
their best mAP values also occur near the tail end of
training.
Figure 3: Total Loss curves of the YOLOv5 Models.
Figure 4: Total Loss curves of the CenterNet Models.
4.4 Model Comparison
The combination of the validation and test sets re-
sulted into a set of 60 images total with 10 images
per class. These 60 images were used for the model
comparison of the seven models. Subsequently, Table
3 depicts a summary of the resulting classification ac-
curacies per model and per class that were measured.
Each cell contains the number of correctly predicted
images, with respect to the model and class, out of the
total number of images present in the class along with
the corresponding accuracy.
For ease of readability, the six classes of the rela-
beled FMLD dataset will be abbreviated in the sub-
sequent sections. The abbreviations are namely Front
- Medical Mask (F-MM), Front - Non Medical Mask
(F-NMM), Front - No Mask (F-NoM), Side - Medical
Mask (S-MM), Side - Non Medical Mask (S-NMM),
and Side - No Mask (S-NoM).
Bearing these technicalities in mind, the Mo-
bileNetV1 model seems to be an anomaly as it per-
formed poorly. It managed to predict nine of the
ten images correctly for the F-NMM and S-NMM
classes. However, it failed to predict any of the ten
images for the F-MM and S-MM classes. Further ob-
servation revealed that the reason behind this behavior
was that the MobileNetV1 model was classifying ma-
jority of the images as Non Medical Mask hence the
high accuracy in these classes. In the end, it managed
to predict 21 of the 60 images in the combined vali-
dation and test set correctly which gives it an overall
Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces
467

Table 3: Summary of Classification Accuracies.
Model Front - Medical Mask Front - Non Medical Mask Front - No Mask Side - Medical Mask Side - Non Medical Mask Side - No Mask Overall
MobileNetV1
(Lopez et al., 2021)
0/10
Accuracy = 0%
9/10
Accuracy = 90%
1/10
Accuracy = 10%
0/10
Accuracy = 0%
9/10
Accuracy = 90%
2/10
Accuracy = 20%
21/60
Accuracy = 35%
ResNet50
(Lopez et al., 2021)
9/10
Accuracy = 90%
10/10
Accuracy = 100%
6/10
Accuracy = 60%
7/10
Accuracy = 70%
10/10
Accuracy = 100%
4/10
Accuracy = 40%
46/60
Accuracy = 76.67%
VGG16
(Lopez et al., 2021)
10/10
Accuracy = 100%
10/10
Accuracy = 100%
6/10
Accuracy = 60%
4/10
Accuracy = 40%
10/10
Accuracy = 100%
3/10
Accuracy = 30%
43/60
Accuracy = 71.67%
YOLOv5 Small
(This Study)
9/10
Accuracy = 90%
9/10
Accuracy = 90%
10/10
Accuracy = 100%
9/10
Accuracy = 90%
10/10
Accuracy = 100%
8/10
Accuracy = 80%
55/60
Accuracy = 91.67%
YOLOv5 Medium
(This Study)
10/10
Accuracy = 100%
8/10
Accuracy = 80%
10/10
Accuracy = 100%
8/10
Accuracy = 80%
10/10
Accuracy = 100%
10/10
Accuracy = 100%
56/60
Accuracy = 93.33%
CenterNet Resnet50
V1 FPN 512x512
(This Study)
10/10
Accuracy = 100%
10/10
Accuracy = 100%
9/10
Accuracy = 90%
9/10
Accuracy = 90%
10/10
Accuracy = 100%
9/10
Accuracy = 90%
57/60
Accuracy = 95%
CenterNet HourGlass104
512x512
(This Study)
10/10
Accuracy = 100%
10/10
Accuracy = 100%
10/10
Accuracy = 100%
10/10
Accuracy = 100%
9/10
Accuracy = 90%
8/10
Accuracy = 80%
57/60
Accuracy = 95%
Bold = Highest Value in Column
accuracy of 35%.
Moving to the ResNet50 model, it had a generally
acceptable performance. The model managed to pre-
dict all of the images correctly in the F-NMM and S-
NMM classes. However, accuracy decreased with the
F-NoM, S-MM, and S-NoM classes. The ResNet50
model had an accuracy of 76.67% all-in-all by pre-
dicting 46 of the 60 images correctly. It therefore
has the highest accuracy out of the three models from
(Lopez et al., 2021).
The VGG16 model also had an acceptable over-
all performance. It correctly predicted all of the im-
ages in the F-MM, F-NMM, and S-NMM classes.
This has one more perfectly predicted class than that
of the ResNet50 model but the VGG16 model had
lower accuracy in the rest of the classes particularly
in the S-MM and S-NoM classes. For this reason, it
only has an overall accuracy of 71.67% which puts
it second to the ResNet50 model. This therefore
makes the order of the most accurate models com-
ing from (Lopez et al., 2021) (from most to least)
be the ResNet50 model in first followed by VGG16
and the MobileNetV1 in last. This is to be expected
as (Agarwal and Mittal, 2019) and (Bianco et al.,
2018) describe that the MobileNetV1 model intends
to be lightweight and have lower accuracy while the
ResNet50 and VGG16 models are deeper or have
more layers and focus more on having high accu-
racy. Furthermore, the ResNet50 model tends to have
higher accuracy than the VGG16 model because the
ResNet50 model has an architecture that is designed
to solve the vanishing or exploding gradient problem.
Proceeding to the YOLOv5 Small model, it can
be said to have higher performance when compared
to the other models. It predicted all of the images
correctly in the F-NoM and S-NMM classes while its
lowest scoring class was eight which is the S-NoM
class. The rest of the classes had nine out of the ten
images predicted correctly. It therefore has an overall
accuracy of 91.67% by predicting 55 of the 60 images
correctly.
As for the YOLOv5 Medium model, it too had
significantly high performance compared to the other
models. All of the images in the F-MM, F-NoM, S-
NMM, and S-NoM classes were predicted correctly.
The rest of the classes had eight of the ten images
predicted as correct. Thus, it managed to predict 56
of the 60 images correctly, one more image than the
YOLOv5 Small model, giving it an overall accuracy
of 93.33%.
This leads to the CenterNet Resnet50 V1 FPN
512x512 model which had the highest overall per-
formance. It only managed to perfectly predict three
classes (F-MM, F-NMM, S-NMM) but the rest of the
classes had nine out of the ten images correctly pre-
dicted. Fifty-seven (57) out of the 60 images were
then predicted as correct resulting in an overall accu-
racy of 95%.
Lastly, the CenterNet HourGlass104 512x512
model also ties for the highest performance. The
model predicted four classes perfectly namely the F-
MM, F-NMM, F-NoM, and S-MM classes. Nine
out of the ten images for the S-NMM class were
correctly predicted while the S-NoM class had eight
correct predictions. It too managed to correctly
predict 57 of the 60 images equalling the Center-
Net Resnet50 V1 FPN 512x512 model and there-
fore also giving it an overall accuracy of 95%. The
order now of the most accurate models (from most
to least of all seven models) comes to the Center-
Net Resnet50 V1 FPN 512x512 and CenterNet Hour-
Glass104 512x512 models tied for first, followed
by the YOLOv5 Medium model, YOLOv5 Small,
ResNet50, VGG16, and finally MobileNetV1. Again,
these are expected because the CenterNet models
are focused on performance while the YOLOv5
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
468

models focus more on speed. The CenterNet and
YOLOv5 models should also have higher accuracies
than the ResNet50, VGG16, and MobileNetV1 mod-
els from (Lopez et al., 2021) because the Center-
Net and YOLOv5 models are trained on the relabeled
FMLD dataset while the ResNet50, VGG16, and Mo-
bileNetV1 models are trained on a different dataset
which is the reclassified MAFA dataset also found in
(Lopez et al., 2021).
In summary, these results support the main ratio-
nale behind this study which was to find out if train-
ing deep learning models to also consider side view
images of the face for face mask detection can im-
prove the robustness of the front-facing face mask de-
tection models that were already trained in (Lopez
et al., 2021). It can be observed in Table 3 that
the three models from (Lopez et al., 2021) (Mo-
bileNetV1, ResNet50, VGG16) had lower accuracy
when it comes to detecting face masks from the side
of a face. An exception would be the Side - Non Med-
ical Mask class where high accuracy was surprisingly
observed. Nevertheless, the four models (YOLOv5
Small, YOLOv5 Medium, CenterNet Resnet50 V1
FPN 512x512, CenterNet HourGlass104 512x512) of
this study that were trained to also consider side view
images of the face had indeed gained an improve-
ment in accuracy when detecting face masks from the
side of a face. Comparing the best overall accuracy
from the models in (Lopez et al., 2021) (Resnet50
with 76.67%) with the models of this study (Center-
Net Resnet50 V1 FPN 512x512 and CenterNet Hour-
Glass104 512x512 with 95%), an overall improve-
ment of ≈ 20% in accuracy was noticed.
5 CONCLUSION AND
RECOMMENDATIONS
To conclude, the goal of this study was to improve
upon the robustness of the deep learning models that
were already created in (Lopez et al., 2021) for face
mask detection. These models were intended to aid
in monitoring compliance by checking if a person is
wearing a mask or not, and if so, check if the mask is
medically approved or not. They were limited how-
ever to only being trained in detecting face masks of
those who look directly into the camera. Thus im-
provements were sought after by finding out if train-
ing new deep learning models to also consider side
view images of the face can indeed improve upon
the robustness of the front-facing face mask detec-
tion of the models from (Lopez et al., 2021). In do-
ing so, dataset relabeling was performed resulting in
the relabeled FMLD dataset with 300 images in to-
tal which are distributed equally across six classes
namely Front - Medical Mask, Front - Non Medical
Mask, Front - No Mask, Side - Medical Mask, Side
- Non Medical Mask, and Side - No Mask. The rela-
beled FMLD dataset was then split into train, valida-
tion, and test image sets. Four deep learning models
were then trained on the training set of images namely
the YOLOv5 Small, YOLOv5 Medium, CenterNet
Resnet50 V1 FPN 512x512, and CenterNet Hour-
Glass104 512x512 models. Afterwards, these four
models were compared with the three models (Mo-
bileNetV1, ResNet50, VGG16) from (Lopez et al.,
2021) by measuring their classification accuracies on
the combined validation and test set of the relabeled
FMLD dataset. Results show that the four models of
this study that were trained to also consider side view
images of the face exhibited improvements in accu-
racy when detecting face masks from the side of a
face. An overall increase of ≈ 20% was observed with
the best models of this study against the best models
from (Lopez et al., 2021) in classifying frontal and
side view images. To this end, the goal of this study
was achieved and it can therefore be said that the four
models of this study are more robust than the models
from (Lopez et al., 2021) when it comes to face mask
detection.
As for future improvements to this study, possi-
ble recommendations can include the creation of a
larger dataset containing more images since the re-
labeled FMLD dataset of this study is relatively small
with only 300 images in total for six classes leading
to 50 images per class. Incorporating ethnicity and/or
time of day can also be added to this larger dataset. A
more exhaustive list of deep learning models can be
further benchmarked as well to find the best perform-
ing one. Another possible route would be to examine
the effects of the different data augmentations that are
available for use with the four models of this study as
they were mainly left at the default settings. Lastly, at-
tempting to produce a real-world application or setup
by extending the methodology of this study serves as
an additional possible avenue for continuation.
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