关键词: 低光照图像增强, 人脸识别, 注意力机制, 特征融合, 对抗性数据增强

Abstract: Images in real-world scenarios are easily affected by external lighting conditions or camera parameters, resulting in issues such as low overall brightness, poor visual effects, and high noise levels. These problems lead to difficulties in subsequent face recognition tasks, causing engineering challenges. To address this, a low-light adaptive face recognition network based on feature fusion, named LLANet (Low Light Adaptive face recognition Network), was proposed for face recognition tasks in low-light scenarios. LLANet was composed of four parts: a decomposition subnet, a restoration subnet, an adjustment subnet, and a backbone network. Low-light and normal-light images were used as inputs. First, based on Retinex theory, the images were decomposed into corresponding illumination and reflection maps. The illumination map was input into the adjustment subnet, where an attention mechanism was introduced to focus on lighting features, enhancing the performance of low-light image enhancement and ensuring the quality of the enhanced images. The reflection map was input into the restoration subnet for detail restoration and noise reduction, effectively addressing the degradation and noise issues of the reflection map in low-light images. The outputs of the adjustment and restoration subnets were fused to obtain the enhanced feature map. Then, to accomplish downstream recognition tasks and prevent overfitting of lighting features and inaccuracies in face feature extraction, a weighted feature fusion strategy was adopted to combine the original face features extracted by the backbone network with the enhanced feature map, resulting in a feature map with richer information. Finally, an adversarial data augmentation strategy was introduced during training to generate more challenging samples, addressing the ill-posed problem while reducing alignment errors caused by low-light images during the face detection phase and further improving network performance. Experiments on CASIA-FaceV5, YaleB, and SoF low-light face datasets demonstrate that the recognition rate of LLANet reach 94.67%, 98.22%, and 97.24%, respectively. Compared with AroFace, the recognition rate improved by 2.14, 1.58, and 2.10 percentage points on the three datasets, respectively. The results indicate that LLANet achieves high recognition accuracy in low-light scenarios.

Key words: Low-light Image Enhancement, Face Recognition, Attention Mechanism, Feature Fusion, Adversarial Data Enhancement