Face Keypoint Detection Method Based on Blaze_ghost Network

Abstract

The accuracy and speed of facial keypoint detection are crucial factors for effectively extracting fatigue features, such as eye blinking and yawning. This paper focuses on the improvement and optimization of facial keypoint detection algorithms, presenting a facial keypoint detection method based on the Blaze_ghost network and providing more reliable support for facial fatigue analysis. Firstly, the Blaze_ghost network is designed as the backbone network with a deeper structure and more parameters to better capture facial detail features, improving the accuracy of keypoint localization. Secondly, HuberWingloss is designed as the loss function to further reduce the training difficulty of the model and enhance its generalization ability. Compared to traditional loss functions, HuberWingloss can reduce the interference of outliers (such as noise and occlusion) in model training, improve the model’s robustness to complex situations, and further enhance the accuracy of keypoint detection. Experimental results show that the proposed method achieves significant improvements in both the NME (Normal Mean Error) and FR (Failure Rate) evaluation metrics. Compared to traditional methods, the proposed model demonstrates a considerable improvement in keypoint localization accuracy while still maintaining high detection efficiency.

Keywords

• face keypoint detection
• PFLD
• Blaze_ghost
• HuberWingloss