  2D face recognition encounters challenges in unconstrained environments due
to varying illumination, occlusion, and pose. Recent studies focus on RGB-D
face recognition to improve robustness by incorporating depth information.
However, collecting sufficient paired RGB-D training data is expensive and
time-consuming, hindering wide deployment. In this work, we first construct a
diverse depth dataset generated by 3D Morphable Models for depth model
pre-training. Then, we propose a domain-independent pre-training framework that
utilizes readily available pre-trained RGB and depth models to separately
perform face recognition without needing additional paired data for retraining.
To seamlessly integrate the two distinct networks and harness the complementary
benefits of RGB and depth information for improved accuracy, we propose an
innovative Adaptive Confidence Weighting (ACW). This mechanism is designed to
learn confidence estimates for each modality to achieve modality fusion at the
score level. Our method is simple and lightweight, only requiring ACW training
beyond the backbone models. Experiments on multiple public RGB-D face
recognition benchmarks demonstrate state-of-the-art performance surpassing
previous methods based on depth estimation and feature fusion, validating the
efficacy of our approach.
