# A Study of Gender Bias in Face Presentation Attack and Its Mitigation
## 4 Experimental Results
### Phase 1: Evaluating Gender Bias in Different CNN-Based Face Presentation Attacks Detection Methods
#### 4.1.2 Exploring Gender Bias Using VGG16  
Experiments to evaluate gender bias conducted using VGG16 included implementing VGG16 on the same three SiW test datasets used in the previous sub-phase with Resnet50. In the first dataset, male frames are 104,019 and female frames are 46,293. The second dataset contains 46,065 images for males and 46,293 images for females. In the last dataset, females have 99,770 frames and males have 104,019 samples. See Table 5.  
VGG16's Experiment 1  
In this experiment, the number of men's samples was more than twice that of women's samples. The results show that the model's accuracy is 99% for females and 97% for males. Table 5 demonstrates that EER and HTER are lower for female subjects than for male subjects. In addition, ACER, BPCER values for males are higher than for females. Moreover, The AUC value for females is slightly better than that for males  
The classification reports shown in Figure 11 Indicates that the values of precision of 1, recall of 0 and 1 are better for females. In terms of f1 scores, males got lower scores than females. VGG16's Experiment 2  
In this experiment, the sizes of test datasets for males and females are very close. Women got a slightly higher number of samples than men. However, testing accuracies for females and males are similar to that of VGG experiment 1. The model has 99% accuracy for females and 97% accuracy for males. The value of AUC is also better for females than for males. Other metrics such as EER and HTER are lower for females. In terms of FAR, FRR, and ACER, the model achieved slightly better results for females than for males. For more detail see Table 5.  
In the classification reports in Figure 12, it is noticeable that females had higher values of precision and recall of 0 and 1 than males. In addition, women have better F1 scores compared to men. Figure 11: Classification report for VGG16’s experiment 1. Figure 10: Classification reports for ResNet50’s experiment 3. VGG16's Experiment 3  
In this experiment, the third dataset was used. As noticed in Table 5, testing accuracies for females and males are still 99% and 97% respectively. Accuracy figures in this experiment are similar to that we got in the two previous experiments. Other metrics such as EER and HTER are slightly lower for male subjects than for female subjects. AUC value for females is greater than that for males. In addition, the model achieved moderately smaller ACER, FAR, FRR values for females than for males. The classification reports in Figure 13 show that females surpassed males in terms of precision of 1 and recall of 0 and 1. In addition, reports illustrate that the f1 score for females is slightly better than the f1 score for males. Phase 2: Exploring the Mitigation of Gender Bias in Detecting Face Presentation Attacks Using VGG16 Architecture and DB-VAE  
In this phase two experiments were conducted with only one size of SiW's test dataset. The test dataset included equal number of males and females. Each subject had 3 live and 4 spoof videos. The first experiment uses a standard VGG16 on test data and training it with a custom training function. The second experiment is about building a DB-VAE model with using the standard VGG16 used in experiment one. Both models were trained on the same SiW's training data, see Figure 7. Table 6 shows the results of both experiments. The results in Table 6 show that males and females have similar testing accuracy and AUC in experiment one using VGG16. The values of other metrics like HTER, EER, FRR, and ACER are almost similar for both genders. The noticeable differences between males and females are seen in Figures 14 and 15. Figure 12: Classification report for VGG16’s experiment 2. Figure 13: Classification report for VGG16’s experiment 3.