# Deep Learning Mask Face Recognition with Annealing Mechanism
## 4 Experiment Results
### Training

Table 4: Hyperparameters for model training. Figure 7: Learning Rate Convergence Comparison Chart. Figure 8: InceptionResNetV2 Training Loss Comparison Chart. Figure 9: InceptionV3 Training Loss Comparison Chart. Among the three models, the best loss values were obtained using CA2 range LR values from \(1\times 10^{-4}\) to \(1\times 10^{-6}\). Step and exponential are the next best in terms of ST2 and EXP2. The third is a fixed LR value of FLR2 = \(1\times 10^{-4}\). The worst case is the fixed LR value of FLR4 = \(1\times 10^{-6}\). Therefore, we conclude that using the CA mechanism in training is better than step, exponential and fixed LR. CA1, ST1 and EXP1 are not as effective as CA2, ST2 and EXP2. This proves that the convergence from \(1\times 10^{-3}\) is too slow, and even if we use annealing to allow LR to converge automatically, we need to find an optimal boundary value. The best result was obtained by using \(1\times 10^{-4}\) in all four methods. In the following test, four sets of hyperparameters with CA2 range of \(1\times 10^{-4}\)-\(1\times 10^{-6}\), ST2 and EXP2 with Initial LR of \(1\times 10^{-4}\) and fixed LR of FLR2 = \(1\times 10^{-4}\) were used. In the comparison procedure, the Euclidean distance of 128-dimensional vector values between two images is used as a criterion to determine whether they are the same face, and a threshold(th) is used to define in advance as a benchmark to determine whether they are the same face. In the face recognition method of Dlib [45], the same face is identified when the Euclidean distance is less than equal to th = 0.6, and greater than th = 0.6 is not [46]. However, this study is an MFR, which is different from the general face recognition, so we need to find a suitable th value again. This study refers to the practice of [47]. The appropriate way to find th is to use the model after training, with four sets of parameters: FLR2, CA2, ST2 and EXP2. The 500 subjects in the MASK600 training set are sequentially divided into 50 groups, with one group for every 10 subjects. 10 images from each subject were randomly selected and compared with the group of image subjects for 128-dimensional Euclidean distance comparison. Therefore, there are 10 subjects and 100 randomly selected images (including synthetic images and original images) in each group. After 50 groups of calculations, the average was taken. The comparison th values ranged from 0.3 to 0.9 (interval 0.01). Since each subject is randomly selected, the search procedure for each model is performed five times and the final total average is calculated. In addition, due to the unbalanced sample size, the negative samples will be more than the positive samples in a single comparison. Therefore, Accuracy cannot be used as a basis for finding th. Here, the average F1-Score of each model group is used as the evaluation criterion instead. For more rigorous experiments, each model is trained twice. Figure 11 shows the average F1-Score and Accuracy of each model after the first training. It can be observed that due to the imbalance of the positive and negative samples, there is no significant difference  
Figure 10: MobileNetV2 Training Loss Comparison Chart. in Accuracy for each model. The F1-Score differs significantly, so the best F1-Score is used as the basis for finding the appropriate th value. Table 5 shows the best F1-Score of each model after the first training is the corresponding th value. The th values for each model are different, and in most cases the th values are between 0.5 and 0.7. Figure 12 shows the average F1-Score and Accuracy trend of each model after the second training. Table 6. shows the best average F1-Score and its corresponding th value for each model after the second training. It can be observed that the changes are similar to the situation in the first training of the value. Most of the th values are the same or only differ by 0.01. The larger differences were 0.03, 0.02 and 0.05 for InceptionResNetV2+EXP2, InceptionV3+EXP2 and MobileNetV2+FLR2, respectively.