## 5 Evaluation
### Face Verification  
In Section 4.3, the minimum loss of the testing set was 0.2069 for the triplet loss learning method and 1.0612 for the SOTA learning method, but only 0.1833 for the proposed learning method combined with triplet loss. The optimum accuracy was improved by 4% from 93.33% for the triplet loss learning method to 97.33% for the proposed learning method combined with triplet loss. From the testing set, 2500 positive and 2500 negative pairs were generated and used to compare the performance of the three trained models. This procedure was repeated 100 times. The average ROC curves of models for face verification are shown in Figure 10. The five learning methods learned the same architecture introduced in Section 3.1. However, the triplet loss learning method, the ArcFace learning method and the proposed learning method used the online adaptive hard triplet dataset configuration method, but the SOTA learning method used the offline hard triplet dataset configuration method. For this reason, the results of other learning methods are relatively similar compared to the SOTA learning method. In addition, through the ROC curves of Figure 10, it was demonstrated in 100 repeated experiments that the proposed learning method has better performance than the triplet loss learning method and the ArcFace learning method.  
Table 3 sums up the results of face verification. Table 3 shows the performance of the models using the best threshold of each model and the performance of the models using the value of \(\alpha\) used for learning. The best accuracy and the threshold of each model were measured while changing the threshold. The mean of the best accuracy of the model trained with the SOTA learning method was 76.9%, and the mean of the threshold was 0.38. The mean of the best accuracy of the model trained with the triplet loss learning method was 87.0%, and the mean of the threshold was 1.29. The mean of the best accuracy of the model trained with the ArcFace learning method was 86.4%, and the mean of the threshold was 1.31. The best mean of the accuracy of the model trained with the proposed learning method combined with triplet loss was 88.4%, and the mean of the threshold was 2.49. The best mean of the accuracy of the model trained with the proposed learning method combined with ArcFace was 88.8%, and the mean of the threshold was 1.49. As a result, the accuracy of the proposed learning method with ArcFace was 1.8% higher than that  
Figure 9: Mean and variance of \(D\) and \(L\) of models trained with the proposed learning method. (**a**) mean; (**b**) variance.  
of the triplet loss learning method. This means that the error rate is reduced by about 13.9 percent. Some examples of false-positive and false-negative pairs of the proposed learning method are presented, as shown in Figure 11. A false-negative error has occurred when the difference in lighting and angle of the same object is large. In addition, the error occurred when the degree of the face being covered by an object is large.  
Figure 11: Example of face verification of the proposed learning method. (**a**) false-positive pairs; (**b**) false-negative pairs.  
Figure 10: The average receiver operating characteristic (ROC) curves comparing the proposed learning method with the SOTA learning method, the triplet loss learning method and the ArcFace learning method.