A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes
This work addresses face recognition accuracy for security or identification applications, but it is incremental as it builds on existing patch-based methods by adding attribute integration.
The paper tackled improving face recognition by combining patch-based features with learned soft facial attributes, resulting in a 4% and 0.37% increase in Rank-1 accuracy on two datasets compared to the baseline UR2D system.
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.