MultiBioGM: A Hand Multimodal Biometric Model Combining Texture Prior Knowledge to Enhance Generalization Ability

Authentication through hand texture features is one of the crucial directions in biometric identification, and some recognition methods based on traditional machine learning or deep learning have been proposed. However, the generalization ability of these methods is not satisfying due to the different entities, backgrounds, and sensors. In this paper, based on the three modalities of fingerprint, fingervein, and palmprint, the texture prior knowledge extractor (PKE) is innovatively designed as a unified paradigm for texture extraction, aiming to improve the model generalization ability through prior knowledge. The feature vectors of texture images are obtained for matching by a knowledge embedding extractor (KEG) based on the Siamese Network. The credibility algorithm is proposed for multimodal decision-level feature fusion. Cascading PKE and KEG is our proposed multimodal biometric generalization model MultiBioGM. Experimental results on three multimodal datasets demonstrate the effectiveness of our model for biometrics, which achieves 0.098%, 0.024%, and 0.117% EERs on unobserved data.