论文标题
卷积神经网络，具有卷积块注意模块，用于手指静脉识别
Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition
论文作者
论文摘要
卷积神经网络由于其强大的图像特征表示，已成为手指静脉识别领域的一项流行研究。但是，大多数研究人员专注于通过增加CNN的深度和宽度来提高网络的性能，这通常需要高度的计算工作。此外，我们可以注意到，不仅像素在不同频道中的重要性不同，而且像素在同一渠道不同位置的重要性也不同。为了减少计算努力并考虑到像素的不同重要性，我们提出了一个具有卷积块注意模块（CBAM）的轻量级卷积神经网络，以实现手指静脉识别，可以通过注意机制更准确地捕获视觉结构。首先，图像序列被馈入我们旨在改善视觉特征的轻量级卷积神经网络。之后，它学会了借助卷积块注意模块以自适应方式分配特征权重。实验是在两个公开可用数据库上进行的，结果表明，所提出的方法在多模式手指识别中实现了稳定，高度准确且稳健的性能。
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.
