Abstract:
With the outbreak of COVID-19, people worldwide started wearing face masks to cover
their mouths and noses to avoid the negative effects of the pandemic. The conven tional face biometrics systems were not designed to handle masked faces. Some facial
features like the nose and mouth get hidden under the mask, resulting in performance
degradation in face biometrics systems. Several studies also reported this degradation in
face biometric systems performance when a mask is worn. In addition, several methods
involving complex and computationally expensive deep learning models proposed for
solving masked face recognition problems had downgraded performance in unmasked
face recognition settings. Therefore, there was a need for a biometric face recognition
system that could not only recognize faces with good performance in masked scenarios
but has at least the same performance as state-of-the-art in unmasked scenarios. This
thesis proposes the Masked Face Unveiling Model (MFUM) to cope with this problem.
The MFUM works on top of existing face recognition models and is built on the concept
that facial embeddings get corrupted for masked faces. This model makes masked facial
embeddings of a person similar to unmasked facial embeddings of the same person and
different from unmasked facial embeddings of other persons. Different ablation studies
have been conducted using face recognition model backbones, MFUM architectures, and
loss functions. Results are evaluated on the LFW dataset with synthetic masks and a
real-world masked face recognition dataset, i.e., MFR2. We have reported the perfor mance of the proposed model in different unmasked and masked settings using evaluation
metrics adopted globally for reporting the performance of biometrics systems, i.e., Equal
Error Rate, False Match Rate, and Fisher discriminant ratio. The results show that the
MFUM having Face Unveiling Attention Augmented Dense Residual Unit architecture
and trained using Quadruplet loss has outperformed state-of-the-art methods in addition
to other evaluated MFUM architectures with different losses.
