A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes

TL;DR

This paper introduces a novel face recognition signature that combines patch-based features with soft facial attributes learned via deep neural networks, resulting in improved recognition accuracy.

Contribution

It proposes a new signature and matcher that integrate implicit features and explicit attributes, enhancing face recognition performance over existing methods.

Findings

Significant improvement in Rank-1 accuracy on UHDB31 and IJB-A datasets.

Effective combination of patch-based features with soft facial attributes.

Enhanced matching scores by weighting different facial attributes.

Abstract

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.