  In this paper, we propose a new deep framework which predicts facial
attributes and leverage it as a soft modality to improve face identification
performance. Our model is an end to end framework which consists of a
convolutional neural network (CNN) whose output is fanned out into two separate
branches; the first branch predicts facial attributes while the second branch
identifies face images. Contrary to the existing multi-task methods which only
use a shared CNN feature space to train these two tasks jointly, we fuse the
predicted attributes with the features from the face modality in order to
improve the face identification performance. Experimental results show that our
model brings benefits to both face identification as well as facial attribute
prediction performance, especially in the case of identity facial attributes
such as gender prediction. We tested our model on two standard datasets
annotated by identities and face attributes. Experimental results indicate that
the proposed model outperforms most of the current existing face identification
and attribute prediction methods.
