# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 4 Experimental Results
### Main Results
#### 4.2.1 Comparison with Existing Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Common** & **Challenging** & **Fullset** \\ \hline \multicolumn{4}{c}{Inter-pupil Normalization (IPN)} \\ \hline RCPR [33] & 6.18 & 17.26 & 8.35 \\ CFAN [34] & 5.50 & 16.78 & 7.69 \\ ESR [35] & 5.28 & 17.00 & 7.58 \\ SDM [36] & 5.57 & 15.40 & 7.50 \\ LBF [37] & 4.95 & 11.98 & 6.32 \\ CFSS [38] & 4.73 & 9.98 & 5.76 \\
3 DDFA [39] & 6.15 & 10.59 & 7.01 \\ TCDCN [40] & 4.80 & 8.60 & 5.54 \\ MDM [41] & 4.83 & 10.14 & 5.88 \\ SeqMT [42] & 4.84 & 9.93 & 5.74 \\ RAR [43] & 4.12 & 8.35 & 4.94 \\ DVLN [44] & 3.94 & 7.62 & 4.66 \\ CPM [45] & 3.39 & 8.14 & 4.36 \\ DCFE [46] & 3.83 & 7.54 & 4.55 \\ TSR [47] & 4.36 & 7.56 & 4.99 \\ LAB [31] & 3.42 & 6.98 & 4.12 \\ PFLD 0.25X [1] & 3.38 & 6.83 & 4.02 \\ PFLD 1X [1] & 3.32 & 6.56 & 3.95 \\ \hline \multicolumn{4}{c}{**Mobile–distill–prune-0.25X** (vs. PFLD 0.25X [1])} & **3.36** & **6.78** & **3.98** \\  & & (+0.59\%) & (+0.73\%) & (+0.99\%) \\ \multicolumn{4}{c}{**Mobile–distill–prune-1X** (vs. PFLD 1X [1])} & **3.24** (+2.4\%) & **6.54** (+0.3\%) & **3.89** (+1.5\%) \\ \multicolumn{4}{c}{**ResNet-50**} & **3.20** & **6.48** & **3.87** \\ \hline \multicolumn{4}{c}{Inter-ocular Normalization (ION)} \\ \hline PFA-CNN [48] & 5.43 & 9.88 & 6.30 \\ RDR [49] & 5.03 & 8.95 & 5.80 \\ PCD-CNN [32] & 3.67 & 7.62 & 4.44 \\ SAN [30] & 3.34 & 6.60 & 3.98 \\ PFLD 0.25X [1] & 3.03 & 5.15 & 3.45 \\ PFLD 1X [1] & 3.01 & 5.08 & 3.40 \\ \hline \multicolumn{4}{c}{**Mobile–distill–prune-0.25X** (vs. PFLD 0.25X [1])} & **3.02** (+0.3\%) & **5.14** & **3.44** \\  & & (+0.19\%) & (+0.28\%) \\ \multicolumn{4}{c}{**Mobile–distill–prune-1X** (vs. PFLD 1X [1])} & **2.98** & **5.04** & **3.38** \\  & & (+0.99\%) & (+0.78\%) & (+0.58\%) \\ \multicolumn{4}{c}{**ResNet-50**} & **2.96** & **4.96** & **3.34** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Normalized mean error comparison of the 300W dataset’s common subset, challenging subset, and full set. The bold entries represent the model proposed in this paper. In summary, our experiments demonstrate the advantages and disadvantages of different models on the 300W dataset through the combination of regression with a heatmap and the comprehensive application of distillation and pruning techniques. Although mobile-distill-prune-0.25X is slightly inferior to mobile-distill-prune-1X in performance, it achieves a satisfactory balance between model size and detection accuracy. ResNet-50 leads in overall performance, particularly when processing complex facial data. The experimental results verify the effectiveness of our proposed method and provide new insights and methodologies in the field of facial keypoint detection. By combining advanced deep learning techniques such as model distillation, pruning, and efficient neural network architecture design, we can significantly optimize the computational efficiency and applicability of the model while maintaining high accuracy. This provides an important reference for the future deployment of efficient and accurate facial feature point detection models in practical applications.