Toward Cross-Dataset Finger Vein Recognition With Single-Source Data

Finger vein recognition (FVR) has recently gained significant attention due to its high security, contactless acquisition, and reliability. However, the principle of vein imaging and touchless mode makes vein images susceptible to variations caused by different acquisition devices and environmental conditions, resulting in gaps among data from different sources. As recognition systems need to be deployed on various devices under uncontrolled environments, FVR faces the challenge of cross-dataset recognition. Moreover, sharing data from multiple datasets is often infeasible due to privacy and security concerns, leading to that training can only be performed using a single-source dataset. To address cross-dataset FVR with single-source data, this article proposes a comprehensive approach. First, an instance-based adaptive transformation component is introduced to reduce dataset gaps at the instance level by removing uncontrolled bias fields that cause dataset discrepancies, adjusting the latent distribution to be more consistent and enhancing vein patterns. Second, a feature alignment and clustering learning algorithm is employed at the feature level to learn invariant features against intraclass variations and perturbations. Third, to fast deploy the deep model to different target datasets with anticipated recognition performance, a modified extreme learning machine (ELM)-based classifier is adopted as a task-specific classifier to quicken the learning speed and improve the generalization capability of the model. Extensive experiments are conducted on four public finger vein datasets, demonstrating the superiority of the proposed method in addressing cross-dataset FVR challenges.