Abstract:
This paper describes our approach for the Disguised
Faces in the Wild (DFW) 2018 challenge. The task here is to
verify the identity of a person among disguised and impostors images. Given the importance of the task of face verification it is essential to compare methods across a common
platform. Our approach is based on VGG-face architecture
paired with Contrastive loss based on cosine distance metric. For augmenting the data set, we source more data from
the internet. The experiments show the effectiveness of the
approach on the DFW data. We show that adding extra data
to the DFW dataset with noisy labels also helps in increasing the gen 11 eralization performance of the network. The
proposed network achieves 27.13% absolute increase in accuracy over the DFW baseline.
