Face Recognition in Real-world Internet Videos Based on Deep Learning

Though current face recognition systems perform well in relatively constrained scenes, they are often affected by secondary creation of netizens, serious image blurring and abundant posture changes in real-world Internet videos. Focusing on these problems, we propose a face recognition model names Internet Video-based Face Recognition Network (IVFRNet) based on deep learning for real Internet videos. And we propose a weighted loss function to enhance the ability of learned features. To test the model, we construct a small-scale real-world Internet video-based face dataset. The experiment results show that our method outperforms the origin method.