Finger vein recognition based on lightweight convolutional attention model

Abstract

AbstractConvolutional neural networks are a hot research topic in finger vein recognition. However, most existing research focuses on increasing the depth and width of the convolutional neural network to improve the network's performance, which has a specific requirement on the computer's computational power. To reduce the computational burden, a lightweight convolutional attention model (LCAModel) is proposed for finger vein recognition to achieve more accurate visual structure capture by exploiting the sensitivity of the attention mechanism to features. First, the attentional model (AModel) is proposed to extract representative features of images, which mainly utilizes the adaptive mapping on space and channels of the convolutional block attention module (CBAM) to make features distinguishable. In addition, considering the integrity of the features, a convolutional model (CModel) is designed to supplement the features of AModel. Finally, the features obtained from AModel and CModel are fused using an adaptive weighting mechanism to make the features more complete. Here, the obtained features are provided into a support vector machine (SVM) for image classification. The experiments are carried out on two publicly available databases, demonstrating that the proposed network structure requires less computing power and performs better.