# Effective Attention-Based Mechanism for Masked Face Recognition
## 4 Experiments and Results
### Experimental Results  
This section reports the model evaluation results. We performed experiments in the face verification task and used the 10-fold cross-validation technique to evaluate the predictive model by randomly dividing the evaluation dataset into ten partitions: nine partitions are used as a training set whereas the remaining partition is used as a validation set. The model repeated training ten times and used the average of the ten validation results as the recognition accuracy. The model was evaluated on simulated masked face images LFW_m, AgeDB-30_m, CFP-FP_m, and real masked face images MFR2. The model extracts the features of all face pairings and then computes the cosine similarities between the face pairs. The accuracy is expressed as the percentage of right predictions, with the highest accuracy being chosen as the threshold. Table 2 reports measurements of the performance of the model in terms of accuracy, precision, recall, and F1 score metrics. The results show that the proposed method achieved high performance in the face verification task. The average accuracy of 10-fold cross-validation on the LFW_m, AgeDB-30_m, and CFP-FP datasets reached rates of 99.43%, 95.86%, and 97.74%, respectively. MFR2 achieved a rate of 96.75%, since this dataset contains different facial postures, expressions, and cloth masks in different textures and colors. We conducted experiments with other state-of-the-art FR methods. Only the proposed method used the CASIA-WebFace_m dataset, as other methods used the original CASIA-WebFace dataset from scratch. The results of the verification accuracies were compared by validating on the same validation dataset. The recognition accuracy results are listed in Table 3.  
As reported in Table 3, our method yielded better results in both generated masked face images and real masked face images. The accuracy rates with the generated images are high and comparable to the results of the existing FR methods. While the accuracy rates of compared methods drop considerably with real mask images (MFR2), the proposed method maintains similar accuracy throughout all benchmark datasets. Several MFR methods are conducted with their proposed training and validation datasets. To compare the proposed method to current existing MFR methods, this study separated the comparison into two parts: In the first part, we compared the presented method results with the results of other MFR methods, as presented in Table 4. In the second part, another experiment was conducted to compare the current advanced method MFCosface [11] with their masked dataset VGG-Face2_m. We trained the proposed network model using the same VGG-Face2_m and tested with 400 pairs of the MFR2 dataset for face verification. The verification performance of the recognition accuracy, precision, recall, and F1 score results are shown in Table 5. Tables 4 and 5 show that the proposed method performs slightly better than MFCosface [11] for both LFW_m and MFR2 datasets, if it is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline LFW\_m & 99.43 & 99.30 & 99.56 & 99.43 \\ AgeDB-30\_m & 95.86 & 93.83 & 97.82 & 95.78 \\ CFP-FP\_m & 97.74 & 96.77 & 98.69 & 97.72 \\ MFR2 & 96.75 & 96.25 & 97.22 & 96.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of recognition accuracy, precision, recall, and F1 score (%) using CASIA-Webface_m. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Training Set** & **LFW\_m** & **AgeDB-30\_m** & **CFP-FP\_m** & **MFR2** \\ \hline CosFace [4] & CASIA-Webface & 95.23 & 93.40 & 92.21 & 63.00 \\ Softmax [5] & CASIA-Webface & 96.68 & 93.50 & 94.78 & 69.75 \\ ArcFace [5] & CASIA-Webface & 96.85 & 94.10 & 95.10 & 71.87 \\ Proposed method & CASIA-Webface\_m & **99.43** & **95.86** & **97.74** & **96.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of face verification results (%) on validation dataset with different methods. trained with VGGFace2_m.