  Existing convolutional neural network (CNN) based face recognition algorithms
typically learn a discriminative feature mapping, using a loss function that
enforces separation of features from different classes and/or aggregation of
features within the same class. However, they may suffer from bias in the
training data such as uneven sampling density, because they optimize the
adjacency relationship of the learned features without considering the
proximity of the underlying faces. Moreover, since they only use facial images
for training, the learned feature mapping may not correctly indicate the
relationship of other attributes such as gender and ethnicity, which can be
important for some face recognition applications. In this paper, we propose a
new CNN-based face recognition approach that incorporates such attributes into
the training process. Using an attribute-aware loss function that regularizes
the feature mapping using attribute proximity, our approach learns more
discriminative features that are correlated with the attributes. We train our
face recognition model on a large-scale RGB-D data set with over 100K
identities captured under real application conditions. By comparing our
approach with other methods on a variety of experiments, we demonstrate that
depth channel and attribute-aware loss greatly improve the accuracy and
robustness of face recognition.
