FV-DDC: A novel finger-vein recognition model with deformation detection and correction

Abstract

Finger vein recognition has gained widespread attention for personal identification due to its robustness and resistance to forgery. While Convolutional Neural Network (CNN)-based finger vein recognition algorithms have shown promising performance, several challenges remain. Firstly, existing methods often fail to effectively handle complex finger deformations, such as bending and rotation, which frequently occur in real-world applications. Secondly, CNN-based approaches typically require large training datasets, yet the available finger vein datasets are limited in size. To address these challenges, this paper presents a novel CNN-based finger vein recognition algorithm, FV-DDC, incorporating a lightweight finger deformation correction module, FVTN. The FVTN module autonomously learns and corrects finger deformations using matrix transformations, offering a new approach to CNN-based deformation correction. The primary advantages of FV-DDC are twofold: automatic finger deformation correction, which simplifies preprocessing, and data augmentation during deformation correction, reducing the dependency on large datasets. Extensive experiments were conducted on three publicly available datasets to validate the effectiveness of the proposed algorithm. The results show that FV-DDC achieves superior recognition performance, particularly in scenarios involving missing data and deformation interference, with recognition accuracies of 99.62% on HKPU, 99.80% on FV-USM, and 98.74% on SDUMLA.