To address the issues of slow recognition speed,high algorithm complexity and poor perfor-mance of transformer architecture on small datasets in existing finger vein recognition algorithms,a light-weight transformer based bidirectional interactive near-infrared finger vein recognition algorithm was proposed,with a parallel backbone network composed of a lightweight convolutional neural network and an improved transformer architecture for local and global feature extraction of near-infrared finger vein images.A up and down structure was designed that integrated features of different scales on two branches in an interactive manner on the basis of a parallel structure.In order to preserve the local fea-tures and global representation of the near-infrared image to the greatest extent possible,the information extracted from the two branches was concatenated and fused,and the recognition results obtained through the output layer.The experimental results showed that the algorithm had a maximum recognition rate of 99.77%on multiple datasets,with a parameter size of only 1.33 MB.Compared to other novel fin-ger vein algorithms and improved transformer architectures,it further reduced the complexity of the algo-rithm while maintaining a high accuracy.
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