Two-step Data Augmentation for Masked Face Detection and
Recognition: Turning Fake Masks to Real
Yan Yang Aaren, George Bebis and Mircea Nicolescu
Department of Computer Science and Engineering, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, U.S.A.
Generative Adversarial Networks, Data Augmentation, Face Recognition.
The COVID-19 spread raised urgent requirements for masked face recognition and detection tasks. However,
the current masked face datasets are insufficient. To alleviate the limitation of data, we proposed a two-step
data augmentation that combines rule-based mask warping with unpaired image-to-image translation. Our
qualitative evaluations showed that our method achieved noticeable improvements compared to the rule-based
warping alone and complemented results from other state-of-the-art GAN-based generation methods, such as
IAMGAN. The non-mask change loss and the noise input we used to improve training showed effectiveness.
We also provided an analysis of potential future directions based on observations of our experiments.
Computer Vision tasks like recognition, detection,
classification, etc., performed on occluded human
faces, existed even before the COVID-19 outbreak
(Ge et al., 2017). The spread of COVID-19 has ur-
gently imposed performance and robustness require-
ments to such applications. However, multiple latest
masked-face detection or recognition works (Singh
et al., 2021; Montero et al., 2021) claim their models
only as starting points of future transfer learning with
more data, instead of final results, or at least recognize
the data insufficiency problem. The research commu-
nity has built mature datasets of dominantly full faces,
but masked face datasets are still under construction.
For full faces, recognition tasks use single-face
image sets, with every single identity assigned to
multiple facial images (Masi et al., 2018), and de-
tection algorithms work on multi-face scene images
and learn bounding box locations together with an
optional class label for each box (Jain and Learned-
Miller, 2010; Yang and Jiachun, 2018). To improve
the masked-face datasets to facilitate the same learn-
ing approaches, we either collect and annotate raw
data or generate artificial images to augment existing
data (Wang et al., 2020a). In this paper, we focus on
generating artificially masked faces.
Recent works address this problem by modify-
ing unmasked faces into masked. Some use rules
to warp masks onto faces (Wang et al., 2020b; An-
war and Raychowdhury, 2020; Cabani et al., 2021).
One uses Neural Network (NN) to translate unmasked
faces into masked (Geng et al., 2020). The rule-
based methods provide realistic mask textures and
completely avoid the risk of distorting other parts of
faces. However, they often result in bad transitions
between masks and background faces, and the light-
ing on masks often looks unreal. Some rule-based
algorithms (Anwar and Raychowdhury, 2020; Wang
et al., 2021) achieved mask diversity by defining dif-
ferent mask image options, but this diversity is limited
to the predefined mask types. In contrast, NN meth-
ods learn to avoid facial distortions and generate mask
textures, often to a reasonable extent but never with
promise. They provide natural transition, realistic de-
tails, and sometimes more diversity in compensation.
We propose a two-stage approach combining a
rule-based method and image-to-image translation
(I2I). After applying rule-based methods to full faces,
we use an I2I model to translate rule-generated masks
into more realistic ones. Rule-generated mask regions
are calculated to serve as ground truth attention areas,
from which we designed an extra loss to restrict I2I
modifications only to mask regions. The rest of the
paper will call the raw data “full-face” images, the
faces with rule-based masks “rule-based mask” im-
ages, and the final outputs “realistic mask” images.
The applications of fake mask methods include
not only masked face recognition/classification but
also detection that requires multi-face images. By
extracting bounding boxes in multi-face images, con-
verting a portion into masked, and overlaying masked
Aaren, Y., Bebis, G. and Nicolescu, M.
Two-step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real.
DOI: 10.5220/0011037900003209
In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), pages 126-134
ISBN: 978-989-758-563-0; ISSN: 2795-4943
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
faces on original boxes, we transform multi-face im-
ages to serve masked face detection training.
Real-world Datasets. Traditional full-face datasets
embody a large number of faces with high variations
in demographics, head rotations, facial landmarks,
occlusion degrees, facial expressions, etc. (Sagonas
et al., 2016; Karras et al., 2018)
Annotations to these
datasets include categories mentioned above, as well
as subject identities for facial recognition, bounding
boxes for facial detection, and so on.
On the other hand, datasets emphasizing masked
faces are limited in the quantity of data and the vari-
ation of features. (Wang et al., 2020b) developed
two different datasets based on real-world images.
Real-World Masked Face Dataset (RMFD) (Wang
et al., 2020b) for recognition tasks contains 5,000
masked and 90,000 normal faces belonging to 525
people. Masked Face Detection Dataset (MFDD)
(Wang et al., 2020b) contains 24,711 masked face im-
ages for detection tasks, which is currently not pub-
licly accessible
. MAsked FAces (MAFA) by (Ge
et al., 2017) with 30,811 internet images and 35,806
masked faces is the largest real-world masked face
dataset to our best knowledge. It is annotated with rel-
atively abundant information such as face bounding
boxes, mask bounding boxes, mask types, face ori-
entations, occlusion degrees, gender, race, and more,
but MAFA alone is not comparable with all the diver-
sity of currently available full-face datasets. More-
over, no identity information is provided to MAFA,
so it is only for face detection tasks. Besides RMFD,
recognition tasks may also use Masked Faces in Real
World for Face Recognition (MFR2) (Anwar and
Raychowdhury, 2020), a small set of 269 images be-
longing to 53 identities. (Geng et al., 2020) proposed
a two-part Masked Face Segmentation and Recog-
nition (MFSR) dataset. 9,742 masked images were
collected from the web and annotated with mask re-
gions in the first part. The second part contains
11,615 faces, masked or non-masked, for 704 real-
world identities and 300 internet-obtained identities.
Artificially Masked Faces. Some researchers gen-
erated artificial data to augment existing real-world
masked face datasets. Stemmed from RMFD and
MFDD, the same lab proposed Simulated Masked
Face Recognition Dataset (SMFRD) (Wang et al.,
Occlusions here include various types like body parts,
scarves, etc. Medical masks constitute only a tiny portion.
2020b), using a naive copy-and-paste method to put
cartoon mask images onto existing face recognition
datasets. (Anwar and Raychowdhury, 2020) and (Ca-
bani et al., 2021) separately used more sophisticated
methods to warp mask images onto faces based on de-
tected facial landmarks (Sagonas et al., 2016). (An-
war and Raychowdhury, 2020) provided their method
as a MaskTheFace tool for both single- and multi-
face images. (Cabani et al., 2021) published a
single-face dataset, MaskedFace-Net, applying their
rule-based method to Flickr-Faces-HQ (Karras et al.,
2018). MaskedFace-Net consists of Correctly and In-
correctly Masked Face Dataset (CMFD and IMFD)
with about 70,000 synthesized images each. CMFD
is used as our model input. While our paper was
in progress, (Wang et al., 2021) published MLFW
(Masked LFW), which enhanced the landmark-based
warping by extra rule-based improvements on the un-
real lighting and abrupt mask boundaries.
Beyond rule-based methods, (Geng et al., 2020)
proposed an NN model, Identity Aware Mask GAN
(IAMGAN), to synthesize masked faces. It consists
of a CycleGAN-like generation module and an Iden-
tity Preservation (IP) module. The IP module has a
mask region predictor and an identity classifier. The
former predicts mask regions and removes them from
both masked (output) and unmasked (input) images.
The latter classifies identities using the rest parts and
penalizes identity change between input and output
based on information from multiple classifier levels.
GAN Models for I2I. Image to image translation is
a problem of mapping images from one domain into
another, with the intrinsic source content preserved
and the extrinsic target style transferred (Pang et al.,
2021). It has been used in a broad set of scenar-
ios such as image synthesis (Regmi and Borji, 2018),
image segmentation (Guo et al., 2020), style trans-
fer (Zhu et al., 2017), and more. GAN models are
adapted to the I2I tasks by replacing the generator’s
standard random input with source image embeddings
and keeping the discriminator’s function of distin-
guishing synthesized and authentic images.
The earliest versions of GANs for image trans-
lation, such as pix2pix (Isola et al., 2016), require
aligned image pairs as the training data. However,
getting a large number of strictly aligned image pairs
is hard or even impossible in many situations. (Zhu
et al., 2017) proposed CycleGAN using a cyclic loss,
making it possible to train an I2I on unpaired data.
The previously mentioned IAMGAN (Geng et al.,
2020) is an enhanced CycleGAN aiming specifically
to generate masks. Based similarly on CycleGAN,
(Tang et al., 2021) trained the generator to produce at-
tention masks together with the generated image con-
Two-step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real
Figure 1: Examples from dataset B.
tents, which guided the generator to translate individ-
ual objects without changing the background. They
call their general-purpose model “AttentionGAN”.
Our work adapts AttentionGAN (Tang et al.,
2021). The key innovation is that our source im-
ages are not full-face but rule-based mask images, i.e.,
CMFD (Cabani et al., 2021). We use a rule-based
method as the first step and the adapted Attention-
GAN as the second step. We show that: 1. warping
mask images onto full faces provides style guidance
and ground truth attention for better GAN model re-
sults; 2. the GAN model renders the rule-based results
into more realistic details. With the latest advances in
rule-based methods such as (Wang et al., 2021), our
two-step proposal may still improve details.
Like other unpaired I2I models, both training and test-
ing of AttentionGAN require two sets of data, A and
B, A being the source and B being the destination.
Dataset B. We manually cropped web images and ex-
tracted MAFA bounding boxes for dataset B. From
MAFA, we first extracted 8938 single faces with
“simple” masks, full occlusions, sizes of at least 60 ×
60 pixels, and front-facing orientations. Finding that
annotations for MAFA are not accurate, we followed
it by hand-picking a subset that strictly matches the
criteria mentioned above. At the same time, we added
“faces without pitch/roll with light-colored medical
masks” as additional criteria. In this way, we have
a controlled dataset for a more accessible proof of our
concept that a superimposed mask image helps the
subsequent I2I step, and they together achieve better
results than the superimposing alone. With 1597 fi-
nal images from MAFA, we supplemented the small
subset with 98 cropped faces from open-source pho-
tos on
, resulting in a total of
1695 images for training set B. Example images from
B are shown in Figure 1
Dataset A. We use down-sampled CMFD as set
A, which uses uniformly blue medical masks with
rare occasions of misplacement. The downsampling
makes sure the size of set A matches that of set B.
Images credit to Jana Shnipelson et al. on Unsplash
AttentionGAN is designed based on CycleGAN and
shares with CycleGAN the co-training of translation
models in two opposite directions. We use most of the
default parameters in the AttentionGAN repository
with batch size 4, learning rate 0.0002, Adam mo-
mentum 0.5, and weights initialized from Gaussian
distribution w N (0.0, 0.02).
Our input images are all resized to 256 × 256
with no cropping before being fed into the models.
In each translation direction, our generator starts with
a 3-pixel Reflection Padding followed by three convo-
lutional layers with instance normalizations and nine
ResNet blocks. In the convolutional layers, we in-
crease the number of channels at each layer, and in
the ResNet blocks, we keep the number of channels
unchanged. Still in the generator, content tensors and
attention masks are generated after the nine ResNet
blocks in two separate pipelines, each of which con-
sists of three Transposed Convolutional (TC) layers
with two instance normalizations, where the number
of channels increases layer-wise. The content tensors
are activated by tanh and attention masks by softmax.
As shown in Figure 2, the generator produces cor-
responding attention masks for both output content
tensors and the input image tensor. The tensor val-
ues are filtered by their attention masks and summed
up to produce the final generator result.
On the other hand, the discriminator is a three-
layer PatchGAN with kernel size 4 × 4, judging the
input image’s realness based on whether each 4 ×
4 patch looks real. While retaining most of Atten-
tionGAN’s structures, we used multiple ways to adapt
them to our training needs.
4.1 Adding Non-mask Change Loss
As mentioned, the generator in AttentionGAN out-
puts a set of attention masks, which learn to find the
most distinguishing parts between sets A and B unsu-
pervised. However, we obtained sets A and B from
different sources, causing heterogeneity beyond the
facial mask differences. Examples include 1. faces in
set B are more zoomed-in, in most extreme cases with
foreheads partly cut out; and 2. Set B includes more
outdoor scarves and hoods, which often occlude the
lower parts of the masks and the foreheads. These ad-
ditional sources of heterogeneity caused the generator
to produce inaccurate attention. On the other hand,
the inputs for our GAN model are generated by warp-
ing mask images onto full faces, so the mask regions
can be determined by comparing the full-face images
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
Figure 2: Generator architecture in AttentionGAN. Our noise inputs are depicted in red.
with rule-based mask images pixel by pixel. Utilizing
this pixel-by-pixel comparison to improve the misled
attention became one of our improvement directions.
To achieve this, we created an extra “Non-Mask
Change (NMC)” loss that calculates the L1 distances
between the rule-based mask images and the realistic
mask images for all pixels outside the mask regions.
We minimize the sum of this L1 distance and other
losses in AttentionGAN. The non-mask region for
each rule-based mask image is calculated pre-training
as a 256 × 256 boolean tensor, stored with the same
file name as the rule-based mask image but with a
different extension name. During training, these ten-
sor files are paired with their rule-based mask images
and the generated realistic mask images for the NMC
loss calculation. Note that we only calculate the loss
at the training stage, so we do not have to get these
tensor files for any generalized model test or usage
once we finish training. Our model learns to automat-
ically generate the attention mask instead of relying
on ground truth attention masks beyond training.
4.2 Adding Noise
Inspired by StyleGAN (Karras et al., 2018), which
takes random noise input to multiple generator lay-
ers for result diversity, we modified AttentionGAN
to include zero-mean Gaussian noise input of a sim-
ilar style. We first attempted noise input to the
first content-generating TC layer following the nine
ResNet blocks. We tested the noise-tuned model
structure on parameters trained without noise and
found that the generated images remained the same
no matter how we amplified the noise. We concluded
that the first TC layer assimilates noise and does not
map noise input to identifiable features in the final
output. Then, with one TC layer noise-tuned at a
time, we tested different amplitudes of noise input on
the other two content-generating TC layers, using the
same model mentioned above. We found that noise
added to either of the other two TC layers makes a dif-
ference to the model output, producing random noise
pixels as shown in Figure 3.
Figure 3: Feed noise to the last two content-generating lay-
ers without training.
Based on these tests, we trained our final model
using a modified structure: the generator takes zero-
mean Gaussian noise inputs to the last two content-
generating layers, with standard deviations being 1
and 0.2 separately for the second and the last content-
generating layers. We denoted our noise input loca-
tion in Figure 2.
Figure 4: Uniform-colored masks generated at epoch 279.
Section 5.1 shows that models at two different
epochs generated diverse colors after adding the noise
input. In contrast, before adding the noise, our model
output is in uniform color for any single checkpoint,
as shown in Figure 4. Therefore, we conclude that the
noise input effectively results in increased diversity.
Besides diversity, the noise input, together with
the NMC loss, also reduced distortions to non-mask
areas and stabilized training. Before the improve-
ments, with the training process producing one sam-
Two-step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real
Figure 5: Models drastically distort inputs at two different
epochs. Outputs like images 2 and 4 intermittently appear
as the training progresses.
ple per epoch, the samples did include faces mostly
preserved from the input for consecutive epochs.
However, drastically distorted and completely re-
drawn faces, as in Figure 5, also last for epochs often,
interweaving with the preserved faces. In contrast, all
epoch samples stably retained input faces after apply-
ing the improvements, with only local changes fluc-
tuating, proving the increased training stability. The
remaining non-mask changes still exist, with exam-
ples shown in Figure 8, but both the extent of changes
and the stableness of training were improved.
4.3 Transfer Learning and the Training
We used transfer learning from our trial-and-error
experiments. We gradually improved datasets and
methods in our training timeline, but we did not dis-
card previous checkpoints. Instead, we believe that a
checkpoint from training epochs with less ideal model
settings and datasets is better than a random start.
Our initial parameters for the single-face mask
generation task are from a different research topic:
a multi-face image translator trained with the same
AttentionGAN model. These initial weights were
gained by 60 epochs of training on Face Detection
Data Set and Benchmark (Jain and Learned-Miller,
2010) and MAFA data, with hyperparameters in the
original AttentionGAN code set to lambda
= 5,
= 5, and lambda
= 0.2. From each
of FDDB and MAFA, about 25,000 images were se-
lected as training sets A and B separately. We will not
elaborate on the rationale behind our choices since it
belongs to other research. The information here is
only for the reproduction of our result.
Within the single-face mask generation task itself,
our first experiment used all the 9,517 face images ex-
tracted from the MAFA training set, which were an-
notated as 1) fully occluded (with occlusion degree
equal to or higher than three), 2) of “simple” mask
type, and 3) at least 60 × 60 in size. From the analysis
of this first experiment, we found our training set A,
i.e., the CMFD data, is much less diverse in orienta-
tions of faces than set B. To prevent set B’s additional
orientation variations from confusing the model, we
limited the image extraction to only facial orientations
of “front,” “front left,” and “front right, getting 8,938
images for set B. We downsampled set A to match
the number. Our continued training used these 8,938-
image datasets, with starting weights copied from the
model at epoch 60 in our first experiment.
Then we noticed that faces in MAFA also involve
pitch and roll rotations, which, unlike yaw rotations,
are not annotated. On the other hand, CMFD mostly
restricted pitch and roll rotations. Also, masks an-
notated as “simple” in set B are not always simple
medical masks similar to CMFD. Cloth or gauze veils
are also annotated as simple types. We believe limit-
ing the source and destination datasets to have similar
variations in the aspects above may help the model fo-
cus on the target modifications, i.e., the masks. There-
fore, out of the 8,938 images, we manually selected
1,597, which are limited in pitch and roll rotations
with only light-colored medical masks.
Adopting 98 additional real-world masked faces
from online for set B, we finalized our datasets with
1,695 images in each set as described in Section 3.
After this, training and improvements, including in
Sections 4.1 and 4.2, are all based on 1,695 real-world
images and 1,695 CMFD images. The whole training
timeline on the single face task is shown in Table 1.
The training model was updated along the way as
we designed new improvements, but training epochs
on old models were utilized and stacked together. We
might have tried more clear-cut experiments if we had
time, but this methodology has accelerated training
and alleviated that our final datasets are small.
Output from training epochs showed that our model
slowly converged after applying noise input and NMC
loss. We carefully watched this trend and visually
picked two better-performing checkpoints, check-
points 313 and 476, from the later epochs for our test.
5.1 Improvements on Top of CMFD
Test results in Figure 6 show that, compared to CMFD
inputs, epoch 313 provides a diversity of mask colors
that match dataset B’s color distribution. It also shows
better details than CMFD on various aspects:
Fabric folds and resulting irregular mask region
Straps or their connecting points with the masks;
More realistic lighting matching cheek curva-
Visual effects of masks lifted by the nose bridges;
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
Table 1: Transfer learning from trial-and-error experiments. All epochs use Learning Rate 0.0002 and Lambda identity 0.5.
Epochs Lambda
Change Loss
Training Data Selection
Restrictions Added to B
160 10/10 No No 9,517 Simple mask, fully occluded
6190 10/10 No No 8,938 Front facing
91140 8/8 No No 1,695 No pitch/roll, light-colored medical mask
141298 8/8 No Yes 1,695 None
299510 8/8 Yes Yes 1,695 None
More natural transitions from masks to faces.
In epoch 476, as shown in Figure 7, all the diversity
and details mentioned here are rendered in even more
powerful ways. Images such as the top-left and the
bottom-right ones in Figure 7 even learned to partly
put other occlusions, such as hand or veil, in front of
the mask occlusion.
However, epoch 476 also produced more noise
and distortions than epoch 313. We think this is due to
overfitting the small training set. Red and white colors
in the hair and forehead (Figure 8) are likely caused
by faces wearing not only masks but also Christmas
hats appearing in our target training set repeatedly.
Patterns on the bottom parts of masks (Figure 8) may
be caused by a large portion of images with patterned
scarves occluding the masks.
5.2 Comparison with IAMGAN
Both (Geng et al., 2020) and our research used
CycleGAN-based methods to turn full faces into
masked ones. Our differences include:
IAMGAN uses a multi-layer identity loss, while
our NMC loss is pixel-level only. They differ be-
cause IAMGAN aims to keep the person’s identity
after adding a mask, while we want to keep the
images exactly the same except the mask region
to facilitate both recognition and detection tasks.
IAMGAN always predicts the mask regions,
while we utilize ground truth mask regions during
training and only predict it during testing.
IAMGAN works on more diverse data, while we
have pioneer work on constrained datasets.
IAMGAN turns full faces directly into masked
ones, while we require a pre-step and turn fake
masks into more realistic ones after the pre-step.
Performance scores such as Frechet Inception Dis-
tance (FID) (Heusel et al., 2017) and Kernel Incep-
tion Distance (KID) (Bi
nkowski et al., 2018) are usu-
ally used to compare the fidelity of different synthe-
sized datasets, but we lack a real-world masked face
dataset as the baseline. Datasets highly similar to ei-
ther IAMGAN’s or our training data would unfaith-
fully push one party’s score high. Therefore, we ran
our model on some examples shown in the IAMGAN
paper (Geng et al., 2020) and demonstrated a qualita-
tive comparison in Figure 9.
The two models showed similar abilities to retain
non-mask regions. Benefiting from the guidance of
the superimposed fake masks, our model got more
accurate nose bridge positions and occasionally more
details such as fabric folds and connecting points be-
tween masks and straps. However, IAMGAN offered
good fabric and lighting details in many cases, too,
and it offered higher diversity in mask colors.
5.3 Potential Improvements
We believe a more thorough work on datasets would
greatly benefit the results in the future. It would be
best to simultaneously achieve the mutual similarity
between sets A and B, the size, and the diversity.
Making the two datasets, i.e., A and B, more sim-
ilar to each other, with masks being the only source
of heterogeneity, is an alternative approach to improv-
ing attention learning and reducing distortions to non-
mask face areas, complementing the extra loss func-
tion in Section 4.1. As mentioned in Section 3, we
have already limited our data to only a subset from
MAFA and This step is exactly based
on the consideration of limiting irrelevant heterogene-
ity. However, it resulted in a small dataset. While
the steps described in Section 4.3 using larger datasets
to get our initial weights before training on the small
datasets helped alleviate the dataset size problem, get-
ting a more considerable amount of quality data may
provide further improvements.
Future work on datasets may also emphasize in-
creasing mask color/type diversity, but the type di-
versity should be attempted together with increased
model abilities in learning diverse mask shapes. The
cycle loss in our model is better at dealing with point-
to-point mapping with little shape-changing, so it may
be insufficient for the shape diversity. Besides, we
only need the A-to-B model, not the opposite direc-
tion. Therefore, the single-sided domain mapping
proposed by (Benaim and Wolf, 2017), with its dis-
tance constraints substituting the cycle loss, could be
one direction we consider together with an increased
Two-step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real
Figure 6: Results produced by the model at epoch 313. Input and output images are paired side by side.
Figure 7: Results produced by the model at epoch 476. Input and output images are paired side by side.
Figure 8: Noisy output in epoch 476 test result. (a) Red and
white blocks in hair. (b) Patterns on the mask.
Figure 9: Comparing our model with IAMGAN.
IMPROVE 2022 - 2nd International Conference on Image Processing and Vision Engineering
mask type diversity. If we retain the two-directional
training architecture, the distance constraints may
even be used together with the cycle loss.
Besides improving the data, the non-mask penalty
loss itself can be improved in two different ways.
First, besides calculating the loss based on the im-
proper content change, we may compare the ground
truth attention masks directly with the generator-
produced attention masks, taking the differences be-
tween the two as an extra loss. Second, instead of
using a binary tensor indicating whether each pixel is
supposed to be changed or not, we may set a finer-
weighted penalty that punishes pixel changes farther
away from the mask more than those closer to the
mask. Such a weighted penalty would allow more
room for the model to create realistic details in the
transition regions, for example, mask straps and fabric
folds. These improvements to the non-mask penalty
will further increase the learning stability and reduce
improper changes outside the mask.
We aimed at turning full face detection/recognition
datasets into masked face datasets, supplementing the
limited training data for masked face tasks. For this
purpose, we proposed a two-step data augmentation
method, utilizing (Cabani et al., 2021)’s algorithm
to warp mask images onto faces as a pre-step to an
AttentionGAN-like model that generates more realis-
tically masked faces. We applied multiple improve-
ments to the GAN model training and verified their
effectiveness through experimental results. Analyses
of our final results showed that the two-step method
provided noticeable improvements compared to us-
ing a rule-based method alone. Even with the latest
advances of the rule-based method by (Wang et al.,
2021), we still expect an extra I2I step to render
the rule-based results with more details, such as ir-
regular region boundaries caused by fabric folds and
straps. Our results are also comparable with state-
of-the-art NN-only mask generation methods such as
IAMGAN, with complementary details. For exam-
ple, we produced lighting changes and mask stripes or
their connecting points missing in IAMGAN results.
While our current model and the generated images
can be used in masked face detection or recognition
tasks, we have limitations, including patterned noise
caused by overfitting small datasets, the remaining
face distortions, and the lacking of diversity in mask
color and type. Based on discussions about these lim-
itations, we pointed out several directions to generate
even better supplemental training data in the future.
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