  We introduce a deep convolutional neural networks (CNN) architecture to
classify facial attributes and recognize face images simultaneously via a
shared learning paradigm to improve the accuracy for facial attribute
prediction and face recognition performance. In this method, we use facial
attributes as an auxiliary source of information to assist CNN features
extracted from the face images to improve the face recognition performance.
Specifically, we use a shared CNN architecture that jointly predicts facial
attributes and recognize face images simultaneously via a shared learning
parameters, and then we use facial attribute features an an auxiliary source of
information concatenated by face features to increase the discrimination of the
CNN for face recognition. This process assists the CNN classifier to better
recognize face images. The experimental results show that our model increases
both the face recognition and facial attribute prediction performance,
especially for the identity attributes such as gender and race. We evaluated
our method on several standard datasets labeled by identities and face
attributes and the results show that the proposed method outperforms
state-of-the-art face recognition models.
