2D-3D Attention and Entropy for Pose Robust 2D Facial Recognition

Despite recent advances in facial recognition, there remains a fundamental issue concerning degradations in performance due to substantial perspective (pose) differences between enrollment and query (probe) imagery. Therefore, we propose a novel domain adaptive framework to facilitate improved performances across large discrepancies in pose by enabling imagebased (2D) representations to infer properties of inherently pose invariant point cloud (3D) representations. Specifically, our proposed framework achieves better pose invariance by using (1) a shared (joint) attention mapping to emphasize common patterns that are most correlated between 2D facial images and 3D facial data and (2) a joint entropy regularizing loss to promote better consistency—enhancing correlations among the intersecting 2D and 3D representations—by leveraging both attention maps. This framework is evaluated on FaceScape and ARL-VTF datasets, where it outperforms competitive methods by achieving profile ($90^{\circ}+$) TAR @ 1% FAR improvements of at least $\mathbf{7. 1 \%}$ and $\mathbf{1. 5 7 \%}$, respectively.