  Disguised face identification (DFI) is an extremely challenging problem due
to the numerous variations that can be introduced using different disguises.
This paper introduces a deep learning framework to first detect 14 facial
key-points which are then utilized to perform disguised face identification.
Since the training of deep learning architectures relies on large annotated
datasets, two annotated facial key-points datasets are introduced. The
effectiveness of the facial keypoint detection framework is presented for each
keypoint. The superiority of the key-point detection framework is also
demonstrated by a comparison with other deep networks. The effectiveness of
classification performance is also demonstrated by comparison with the
state-of-the-art face disguise classification methods.
