  Due to the instability and limitations of unimodal biometric systems,
multimodal systems have attracted more and more attention from researchers.
However, how to exploit the independent and complementary information between
different modalities remains a key and challenging problem. In this paper, we
propose a multimodal biometric fusion recognition algorithm based on
fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial
Attention Fusion Module, FPV-CSAFM). Specifically, for each pair of fingerprint
and finger vein images, we first propose a simple and effective Convolutional
Neural Network (CNN) to extract features. Then, we build a multimodal feature
fusion module (Channel Spatial Attention Fusion Module, CSAFM) to fully fuse
the complementary information between fingerprints and finger veins. Different
from existing fusion strategies, our fusion method can dynamically adjust the
fusion weights according to the importance of different modalities in channel
and spatial dimensions, so as to better combine the information between
different modalities and improve the overall recognition performance. To
evaluate the performance of our method, we conduct a series of experiments on
multiple public datasets. Experimental results show that the proposed FPV-CSAFM
achieves excellent recognition performance on three multimodal datasets based
on fingerprints and finger veins.
