Touchless Finger Vein and Fingerprint Verification via Exploiting Attention-Based Cross-Domain Fusion

Due to rich textures and ease of acquisition, finger-based biometric features have gained significant attention for personal authentication in recent years. However, the majority of current finger-based authentication techniques predominantly rely on features extracted solely from a single modality, such as fingerprint or finger vein. Additionally, most current authentication methods utilize contact-based capture and identification, which poses a risk of bacterial or viral infection. To overcome these limitations, we advocate for the adoption of touchless multimodal finger features, providing a hygienic and robust authentication solution. Specifically, we design a device which can capture touchless finger vein and fingerprint images from four fingers, creating the THU-FVFP dataset. To the best of our knowledge, the THU-FVFP dataset is the first publicly available dataset that includes touchless finger vein and fingerprint data from four fingers. Subsequently, we introduce the Attention-based Cross-domain Fusion Network (ACFNet), which can leverage both intra and inter-features of finger vein and fingerprint data. To achieve this, we develop an Intra Multi-Level Feature Fusion Module (IMLFFM) for merging features from different layers within a single modality and an Inter Multi-Modal Feature Fusion Module (IMMFFM) for achieving optimal fusion of diverse features. We extensively evaluate the model on the THU-FVFP database, proving its outstanding performance with an equal error rate of 0.07%. The THU-FVFP dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/oneline-wsq/THU-FVFP-Dataset</uri>.