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Convolutional neural networks have become a popular research in the field offinger vein recognition because of their powerful image feature representation.However, most researchers focus on improving the performance of the network byincreasing the CNN depth and width, which often requires high computationaleffort. Moreover, we can notice that not only the importance of pixels indifferent channels is different, but also the importance of pixels in differentpositions of the same channel is different. To reduce the computational effortand to take into account the different importance of pixels, we propose alightweight convolutional neural network with a convolutional block attentionmodule (CBAM) for finger vein recognition, which can achieve a more accuratecapture of visual structures through an attention mechanism. First, imagesequences are fed into a lightweight convolutional neural network we designedto improve visual features. Afterwards, it learns to assign feature weights inan adaptive manner with the help of a convolutional block attention module. Theexperiments are carried out on two publicly available databases and the resultsdemonstrate that the proposed method achieves a stable, highly accurate, androbust performance in multimodal finger recognition.

Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition