Supervised techniques for recognizing detected objects (faces, vehicles, animals, birds etc) are inherently constrained (they can only recognize objects they are trained to recognize) especially in real-time or live videos. This is because of the uncertainty involved due to limited knowledge of future encounters. While current unsupervised techniques do mitigate this issue up to some extent, they themselves are limited due to the assumptions they make (either the number of clusters are known or enough samples exist that represent the true underlying data distribution). In many real-life problems, such as Computer Vision based TV Analytics, we have no prior knowledge available about the entities appearing. Reducing these constraints, in terms of prior assumptions, will allow real-time unsupervised recognition of detected objects.

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 conventional 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. 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 research 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.