  This paper focuses on improving face recognition performance with a new
signature combining implicit facial features with explicit soft facial
attributes. This signature has two components: the existing patch-based
features and the soft facial attributes. A deep convolutional neural network
adapted from state-of-the-art networks is used to learn the soft facial
attributes. Then, a signature matcher is introduced that merges the
contributions of both patch-based features and the facial attributes. In this
matcher, the matching scores computed from patch-based features and the facial
attributes are combined to obtain a final matching score. The matcher is also
extended so that different weights are assigned to different facial attributes.
The proposed signature and matcher have been evaluated with the UR2D system on
the UHDB31 and IJB-A datasets. The experimental results indicate that the
proposed signature achieve better performance than using only patch-based
features. The Rank-1 accuracy is improved significantly by 4% and 0.37% on the
two datasets when compared with the UR2D system.
