  Despite the recent success of convolutional neural networks for computer
vision applications, unconstrained face recognition remains a challenge. In
this work, we make two contributions to the field. Firstly, we consider the
problem of face recognition with partial occlusions and show how current
approaches might suffer significant performance degradation when dealing with
this kind of face images. We propose a simple method to find out which parts of
the human face are more important to achieve a high recognition rate, and use
that information during training to force a convolutional neural network to
learn discriminative features from all the face regions more equally, including
those that typical approaches tend to pay less attention to. We test the
accuracy of the proposed method when dealing with real-life occlusions using
the AR face database. Secondly, we propose a novel loss function called batch
triplet loss that improves the performance of the triplet loss by adding an
extra term to the loss function to cause minimisation of the standard deviation
of both positive and negative scores. We show consistent improvement in the
Labeled Faces in the Wild (LFW) benchmark by applying both proposed adjustments
to the convolutional neural network training.
