Adaptive representation learning and sample weighting for low-quality 3D face recognition

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3D face recognition (3DFR) algorithms have advanced significantly in the past two decades by leveraging facial geometric information, but they mostly focus on high-quality 3D face scans, thus limiting their practicality in real-world scenarios. Recently, with the development of affordable consumer-level depth cameras, the focus has shifted towards low-quality 3D face scans. In this paper, we propose a method for low-quality 3DFR. On one hand, our approach employs the normalizing flow to model an adaptive-form distribution for any given 3D face scan. This adaptive distributional representation learning strategy allows for more robust representations of low-quality 3D face scans (which may be caused by the scan noises, pose or occlusion variations, etc.). On the other hand, we introduce an adaptive sample weighting strategy to adjust the importance of each training sample by measuring both the difficulty of being recognized and the data quality. This adaptive sample weighting strategy can further enhance the robustness of the deep model and meanwhile improve its performance on low-quality 3DFR. Through comprehensive experiments, we demonstrate that our method can significantly improve the performance of low-quality 3DFR. For example, our method achieves competitive results on both the IIIT-D database and the Lock3DFace datasets, underscoring its effectiveness in addressing the challenges associated with low-quality 3D faces.