Deep learning methods have yet to effectively incorporate insights from traditional approaches to extract palmprint-specific features. Moreover, intraclass spatial variation problems, which degrade the recognition performance, have not been adequately addressed. To tackle these limitations, this chapter proposes an Aligned Multilevel Gabor Convolution Network (AMGNet) to identify the informative and salient aspects of the palmprints. The network unifies a multilevel Gabor feature fusion branch with a spatial alignment branch, enabling the joint mining of aligned multilevel features specific to palmprints. Within the feature fusion branch, we incorporate two specialized Gabor convolution modules: One targets the principal lines of the palm, while the other focuses on the wrinkles, augmenting the discriminative power of the acquired features. To enhance the model’s robustness against within-class variations, we design a spatial alignment branch that specifically enables the rectification of palmprints’ spatial positions. In conjunction with this, we introduce a novel direction-based CosAngle loss function to facilitate geometric alignment among samples from the same palms while spatially distancing those from different palms. Extensive experimental results on six benchmark datasets demonstrate that our proposed method outperforms other popular approaches in palmprint recognition tasks.