Abstract: For low-resolution face recognition scenarios demanding high accuracy,efficiency,and lightweight design,this study proposes a HDFGD(Hierarchical Dynamic Feature Generation Distillation) framework.Based on feature redundancy theory and knowledge distillation,the framework constructs a dynamic hierarchical feature generation module to enhance the feature representation capabilities of lightweight student networks.The module expands feature diversity through channel-wise convolutions,employs channel attention mechanisms to focus on critical semantic information,and integrates adaptive compression mechanisms to reduce computational complexity,effectively mitigating semantic gaps across resolution-specific features.Simultaneously,the ArcFace loss function is incorporated to optimize angular-margin constraints in the feature space,thereby strengthening intra-class compactness and inter-class separability,and ultimately improving the accuracy of low-resolution face recognition.Experimental results demonstrate that the HDFGD framework achieves a maximum 2.1% improvement in accuracy over mainstream methods on the CIFAR-100 classification task.It also attains recognition accuracies of 95.87% and 97.02% respectively on two benchmark low-resolution face re-cognition datasets,significantly outperforming existing lightweight face recognition algorithms.Ablation studies confirm that the combination of dynamic feature generation with hierarchical alignment strategies and the ArcFace discriminative supervision mechanism simultaneously achieves triple breakthroughs:model lightweighting,cross-resolution feature robustness,and recognition accuracy enhancement.This framework provides an efficient technical solution for real-time face recognition tasks in resource-constrained scenarios.

Key words: Low-resolution face recognition, Knowledge distillation, Dynamic feature generation, Lightweight, Hierarchical