Abstract:
With the increased interest in face recognition across di erent applications,
the research in this area has
ourished over the past few
decades. However, face recognition with disguise variations has gained
little attention. Faces in unconstrained settings with disguise as an
additional covariate makes this problem challenging. It includes alterations
in facial appearance using disguise accessories. In this thesis,
we propose deep learning based transfer learning approach to handle
the problem of disguise, with the network being ne-tuned on the
proposed loss function termed as \Disguised Loss". We have evaluated
our network on Disguised Faces in Wild (DFW) 2018 dataset [1]
where the proposed algorithm is able to produce competitive results.
We have also introduced a new dataset termed as \DFW2019" which
is an extension of DFW2018 dataset [1]. Apart from the addition of
250 subjects with 3140 images, 250 plastic surgery image pairs and
100 bridal image pairs have also been added. Additional protocols for
plastic surgery face recognition have also been introduced. We have
presented baselines for all the protocols along with the results of the
proposed approach.
