# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 4 Discussion
### Results  
Our results can be found in the following figures. We took the images created from our 3 datasets and created 3 different subsets from each of those. The purpose of creating those subsets was to examine the image recognition accuracy and potential for gender bias for  
Figure 8: Sample images of individuals, recreated from the Spoofing in the Wild (SiW) Database. The individual in the top row is genuine, whereas the individual in the bottom row has been spoofed. each CNN model. Each of these subsets was tested 5 separate times per CNN model. After the results were collected, the median accuracy value obtained was added to the following tables. K-fold cross validation is a superior statistical analysis method, and will be utilized for any future testing of these datasets and CNN models. The three created subsets are Balanced (50% male individuals, 50% female individuals), Male Dominant (80% male individuals, 20% female individuals), and Female Dominant (20% male individuals, and 80% female individuals). For a given CNN model, if the accuracy difference between the male dominant and female dominant subsets is greater than 10%, we consider that there is a high likelihood that the model is affected by gender bias. For the WMCA Dataset, we were able to generate over 3000 datapoints (extracted images) for use in our study. This number of images was smaller than what was generated by the SiW Database, but larger than the image amount for the CASIA-FASD Dataset. The accuracy level of our models was also split, being mostly higher than CASIA-FASD, but markedly lower than SiW. Finally, several of the CNN models tested showed high likelihood of potential gender bias, with 5 out of 8 models having a gender disparity of greater than 10 percent for the male and female dominant image subsets. In Table 5, we can clearly see that both VGG models performed well with accuracy; however, the fall in accuracy for VGG19 concerning the female dominant dataset caused that model to be classified as having the potential for gender bias. On the other side of the accuracy scale, both Xception and AlexNet performed the worst of the group; in addition, both of these models did not do well in terms of potential gender bias, with those models having 11 and 12 percent gender disparity, respectively. For the CASIA-FASD Dataset, we were able to generate over 2500 datapoints (extracted images) for use in our study. This was the smallest number of images generated by any dataset, which could potentially explain the slightly lower accuracy when compared to other datasets in our study. In addition, for the CASIA-FASD Dataset, 5 out of 8 of the CNN models exhibited a propensity for potential gender bias, which tied for the highest percentage of any of our datasets. Looking at Table 6, the VGG models again scored high marks for accuracy. In this round of testing, both VGG-16 and VGG-19 also showed resistance to potential gender bias, with a gender disparity of 5 and 6 percent, respectively. Unfortunately, both AlexNet and Xception again performed poorly in all of the image subset testing; in addition, they also were shown to likely have problems with diverse datasets, as their gender disparity levels were both at 12 percent. For the SiW Database, we generated over 4200 datapoints (extracted images), making it the largest dataset by far. We see higher accuracy levels across the board for our CNN models potentially as a result of the higher number of images available for training the CNN models. The SiW Database has a far higher accuracy level than either the WMCA or CASIA-FASD Datasets, and as is visible in Table 6, the disparity between the male dominant and female dominant subsets was less than 10 percent for 5 out of the 8 CNN models.