  Convolutional neural networks have become a popular research in the field of
finger vein recognition because of their powerful image feature representation.
However, most researchers focus on improving the performance of the network by
increasing the CNN depth and width, which often requires high computational
effort. Moreover, we can notice that not only the importance of pixels in
different channels is different, but also the importance of pixels in different
positions of the same channel is different. To reduce the computational effort
and to take into account the different importance of pixels, we propose a
lightweight convolutional neural network with a convolutional block attention
module (CBAM) for finger vein recognition, which can achieve a more accurate
capture of visual structures through an attention mechanism. First, image
sequences are fed into a lightweight convolutional neural network we designed
to improve visual features. Afterwards, it learns to assign feature weights in
an adaptive manner with the help of a convolutional block attention module. The
experiments are carried out on two publicly available databases and the results
demonstrate that the proposed method achieves a stable, highly accurate, and
robust performance in multimodal finger recognition.
