Finger Vein and Inner Knuckle Print Recognition Based on Multilevel Feature Fusion Network

Multimodal biometric recognition involves two critical issues: feature representation and multimodal fusion. Traditional feature representation requires complex image preprocessing and different feature-extraction methods for different modalities. Moreover, the multimodal fusion methods used in previous work simply splice the features of different modalities, resulting in an unsatisfactory feature representation. To address these two problems, we propose a Dual-Branch-Net based recognition method with finger vein (FV) and inner knuckle print (IKP). The method combines convolutional neural network (CNN), transfer learning, and triplet loss function to complete feature representation, thereby simplifying and unifying the feature-extraction process of the two modalities. Dual-Branch-Net also achieves deep multilevel fusion of the two modalities' features. We assess our method on a public FV and IKP homologous multimodal dataset named PolyU-DB. Experimental results show that the proposed method performs best and achieves an equal error rate (EER) of the recognition result of 0.422%.

Automated summary

In this study, we propose a dual-branch network-based recognition method for multimodal biometric recognition using finger vein (FV) and inner knuckle print (IKP). The method combines convolutional neural network, transfer learning, and triplet loss function to complete feature representation and achieves deep multilevel fusion of the two modalities' features.