Palm vein recognition has attracted recently wide attention thanks to its robust feature representation and high accuracy. Despite advancements in the literature, however, existing solutions suffer from the following issues: 1) Insufficient large-scale data for deep learning-based recognition of vein biometrics, resulting in decreased generalization performance and model accuracy. 2) Lack of methods based on machine learning convolutional neural networks capable of capturing the global receptive field for vein biometric recognition. In addressing these issues, this paper proposes a method to acquire the global receptive field, termed GANet, which extracts vein features using Gabor filters and computes an attention mechanism to capture the global receptive field for downstream palm vein recognition models. Initially, vein features are extracted using multi-scale fixed Gabor filters and multi-scale adaptive Gabor filters. Subsequently, self-attention mechanisms are employed to compute relationships between blocks to obtain the global receptive field. To perform recognition, the Euclidean distance between feature vectors is then computed. Our experiments on three datasets show that our approach outperforms existing palm vein recognition methods.