遮挡条件下的人脸年龄估计研究
Research on Facial Age Estimation under Occlusion Conditions

在日常生活中，口罩遮挡口部和鼻部、眼镜遮挡眼睛等情况对基于人脸的年龄估计模型造成了较大的挑战。为应对这一问题，本文设计了一种基于知识蒸馏的自监督特征重建模型。由于缺少包含年龄标记的遮挡人脸数据集，我们选择在无遮挡的原始人脸图像数据集上合成口罩和眼镜作为遮挡数据集，并通过自监督学习的方式实现特征重建。在方法中，我们使用主流的年龄估计模型作为教师模型，训练一个结构相同但附加了特征重建模块的学生模型。通过知识蒸馏，将教师模型的知识迁移至学生模型，并利用特征重建模块对遮挡区域进行特征补全，以提升模型对遮挡人脸图像的年龄估计效果。实验结果表明，提出的方法显著提升了模型在遮挡图像上的年龄估计性能。特征重建模块有效缓解了常见遮挡对年龄估计的负面影响，从而增强了模型的实用性和鲁棒性。
In daily life, situations where masks cover the mouth and nose, and glasses cover the eyes, pose significant challenges to facial based age estimation models. To improve the robustness of the facial age estimation model under occlusion, this paper proposes a self-supervised feature reconstruction model based on knowledge distillation. Due to the lack of age labeled occluded face datasets, we chose to synthesize masks and glasses as occluded datasets on the original unobstructed face image dataset, and achieved feature reconstruction through self-supervised learning. In the method, we use mainstream age estimation models as teacher models and train a student model with the same structure but additional feature reconstruction modules. By knowledge distillation, the knowledge of the teacher model is transferred to the student model, and the feature reconstruction module is used to complete the features of the occluded area, in order to improve the age estimation effect of the model on occluded face images. The experimental results show that the proposed method significantly improves the age estimation performance of the model on occluded images. The feature reconstruction module effectively alleviates the negative impact of common occlusion on age estimation, thereby enhancing the practicality and robustness of the model.