# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 5 Conclusions  
This paper addressed the robustness of face keypoint detection, focusing on effective and efficient detection in challenging environments such as occlusion and inversion. Our key contributions are as follows. Effectiveness enhancement: we proposed a novel approach that combines regression and heatmap training, inspired by the concept of human keypoints. This method significantly improves the accuracy and reliability of face keypoint detection, even under extreme conditions. Efficiency improvement: to reduce the model's computational load, we introduced a compression method that integrates distillation and pruning. This approach effectively minimizes the model size while maintaining high performance, making it suitable for deployment on resource-constrained devices. Integrated technique: we successfully combined regression, heatmap, pruning, and distillation techniques into a cohesive framework. Our comprehensive experiments demonstrated the robustness and superiority of our method compared to existing approaches, especially in terms of speed and accuracy. Experimental validation: through detailed experiments, we compared our method with existing deep learning approaches and used significant statistical analyses to demonstrate the robustness and generalization ability of our model's performance. In ablation experiments and hyperparameter studies, we examined the impact of combining regression with heatmap training and pruning with distillation on the algorithm's accuracy. Finally, we conducted a comparative study on model size, computational speed, and mobile device detection. The research data validated the efficiency, effectiveness, and lightweight nature of the method proposed in this paper. Our findings have important practical implications for real-time applications such as facial recognition, augmented reality, and intelligent monitoring. The ability to detect face keypoints accurately and efficiently, even in extreme conditions, enhances the usability \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mobile** & **HUAWEI Mate9 Pro** & **Xiaomi MI 9** & **HUAWEI P30 Pro** \\ \hline
**CPU** & Kirin960 & Snapdragon855 & Kirin980 \\
**Frequency** & \(4\times 2.4\) GHz + & \(1\times 2.84\) GHz + & \(2\times 2.6\) GHz + \\
**RAM** & \(4\times 1.8\) GHz & \(3\times 2.42\) GHz + \(4\times 1.78\) GHz & \(2\times 1.92\) GHz + \(4\times 1.8\) GHz \\
**ResNet50** & \(87\) ms & \(63\) ms & \(49\) ms \\
**Mobile–distill–prune-1x** & \(31\) ms & \(18\) ms & \(14\) ms \\
**Mobile–distill–prune-0.25x** & \(19\) ms & \(13\) ms & \(11\) ms \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing model performance on three different mobile devices. The bold entries represent the model proposed in this paper. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **SDM [36]** & **SAN [30]** & **LAB [31]** & **PFLD 0.25X [1]** & **PFLD IX [1]** & **mobile–distill–prune-0.25x** & **mobile–distill–prune-1x** \\ \hline
**Size (Mb)** & \(10.1\) & \(270.5+528\) & \(50.7\) & \(2.1\) & \(12.5\) & \(2.5\) & \(7.8\) \\  & & & \(1.2\) ms (C) & \(6.1\) ms (C) & \(2\) **ms (C)** & \(4.5\) ms (C)** \\
**Speed** & \(16\) ms (C) & \(343\) ms (G) & \(60\) ms (G*) & \(1.2\) ms (G) & \(3.5\) ms (G) & \(1.9\) ms (G) & \(3.6\) ms (G) \\  & & & \(7\) ms (A) & \(26.4\) ms (A) & \(12\) ms (A) & \(18.7\) ms (A) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of model size and processing speed with existing methods. The bold entries represent the model proposed in this paper. and reliability of these applications. Our model's reduced computational complexity and storage requirements make it ideal for deployment on mobile devices and other platforms with limited resources. While our approach demonstrates robustness in occlusion and inversion scenarios, there may still be edge cases where performance could be improved. Future research should focus on enhancing the model's ability to handle more complex and varied occlusions.