Dual-Branch Cross-Scale Texture Feature Fusion for Low-Resolution Face Recognition

Face images captured often suffer from low resolution and significant information loss. Traditional methods struggle to effectively extract local key features, leading to suboptimal recognition accuracy. To address these challenges, this paper introduces a novel approach based on dual-branch cross-scale texture feature fusion for low-resolution face recognition (DCSF-LR). The proposed method enhances the focus on facial details through local texture feature fusion and a dual-branch cross-scale attention module, enabling the extraction of richer facial features. Additionally, knowledge distillation is utilized to transfer knowledge from high-resolution face images to the low-resolution face recognition model. A newly designed loss function is introduced to facilitate effective knowledge transfer, better adapting the model to low-resolution face recognition tasks in uncontrolled environments. Moreover, a degradation module is developed to generate realistic low-resolution face images for training the student model, thereby improving its adaptability in real-world scenarios. Extensive experiments on the TinyFace and AgeDB-30 data sets demonstrate the effectiveness of the proposed method. It achieves 90.04% accuracy at $28\times 28$ resolution on AgeDB-30 and 57.73% (ACC@5) on TinyFace, surpassing existing methods in both accuracy and generalization.