  With the improvement of arithmetic power and algorithm accuracy of personal
devices, biological features are increasingly widely used in personal
identification, and palm vein recognition has rich extractable features and has
been widely studied in recent years. However, traditional recognition methods
are poorly robust and susceptible to environmental influences such as
reflections and noise. In this paper, a convolutional neural network based on
VGG-16 transfer learning fused attention mechanism is used as the feature
extraction network on the infrared palm vein dataset. The palm vein
classification task is first trained using palmprint classification methods,
followed by matching using a similarity function, in which we propose the
multi-task loss function to improve the accuracy of the matching task. In order
to verify the robustness of the model, some experiments were carried out on
datasets from different sources. Then, we used K-means clustering to determine
the adaptive matching threshold and finally achieved an accuracy rate of 98.89%
on prediction set. At the same time, the matching is with high efficiency which
takes an average of 0.13 seconds per palm vein pair, and that means our method
can be adopted in practice.
