Study of Full-View Finger Vein Biometrics on Redundancy-compensated 3D Reconstruction and Frequency-Spatial Coupling...

Full-view 3D finger vein (3DFV) biometrics is a significant advancement in vein biometrics. Nevertheless, its progress is fundamentally constrained by the limited robustness of 3D reconstruction. This challenge stems primarily from the inherent limitations of consumer-grade capture devices, which introduce imaging errors that are virtually impossible to fully circumvent. Meanwhile, how to adequately and efficiently extract features from the reconstructed 3DFV is also a direction that has not been thoroughly explored. To address these challenges, this paper introduces a robust 3DFV reconstruction method with a quality assessment mechanism and an adaptive feature extraction network. For reconstruction, we propose Redundancy-Compensated 3DFV (RC-3DFV), which incorporates a redundancy compensation term during reconstruction to mitigate imaging errors from consumer-grade devices. The reconstructed 3DFVs are unfolded into 3D finger texture (3DFT) and 3D finger shape (3DFS) maps to reduce dimensionality. A fast 3DFV Quality Assessment (3DFV-QA) mechanism is then applied to select the optimal reconstruction result. For feature extraction, we present 3DFVFSNet, an enhanced version of FVFSNet that effectively couples frequency-domain and spatial-domain convolutions. This design adequately captures both the global characteristics of 3DFS maps and the local details of 3DFT maps. We have conducted a series of rigorous experiments to evaluate the effectiveness of the proposed methods, including the 3DFV reconstruction experiment and 3DFV authentication experiment. Experimental results demonstrate the competent performance and efficiency of our proposed methods.