Hong Xu
ABSTRACT
On the basis of the original U-Net and ResNet finger vein segmentation methods, this paper proposes a new finger vein segmentation method based on the combination of the above two methods. This method completes feature extraction by using depth residual network instead of U-Net. Using extended convolution instead of traditional convolution solves the risk of loss of finger vein information extracted by pooling layer, so that the method can extract finger vein veins more accurately without pooling. The algorithm uses the Mish constructor to replace the ReLU constructor to strengthen the anti-compression ability of the model and make vein image more continuous. From the comparison of experimental results, it is not difficult to see that the segmentation accuracy of the newly proposed Veinseg-Net network on multiple finger vein databases are better than those of other depth learning segmentation methods. The good segmentation accuracy of finger vein image can help the finger vein recognition process in the subsequent environment intelligence.
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