# Examining Gender Bias of Convolutional Neural Networks via Facial Recognition
## 3 Methodology
### Convolutional Neural Networks Detailed
#### 3.1.4 VGG-16/VGG-19  
The initial VGG architecture was proposed back in 2014, by [PERSON] and [PERSON], both of the world-famous Oxford Robotics Institute. Short for Visual Geometry Group, both VGG-16 and VGG-19 are extremely high pedigree CNN models that specialize in both image classification, as well as object localization [24]. Much like its compatriot AlexNet, VGG-16 competed and performed quite well in the 2014 ILSVRC, where it placed second in the image classification challenge, and attained a stunning first place in the object localization challenge.  
Figure 4: Inception version 2 architecture with dimension reductions, based on the original architecture.  
VGG is perhaps the most well-known of all of the different CNN model groups, due to the impressive results it has been able to obtain nearly since its inception into the field. VGG uses 3-by-3 convolutions, and obtains quite impressive accuracy, even with an architecture that is smaller than most similar CNN models. While fairly similar, the main difference between VGG-16 and VGG-19 consists of the total number of depth layers in each architecture; of which the majority of those are convolution layers, which should not come as a surprise. A recreation of the initial VGG architecture, which includes both the VGG-16 and VGG-19 model, can be viewed in Table 3[39].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**ConvNet Configuration**} \\ \hline A & A-LRN & B & C & D & E \\ \hline
11 weight & 11 weight & 13 weight & 16 weight & 16 weight & 19 weight \\ layers & layers & layers & layers & layers & layers \\ \hline \multicolumn{5}{c}{**input (224 \(\times\) 224 RGB Image)**} \\ \hline conv3-64 & conv3-64 & conv3-64 & conv3-64 & conv3-64 & conv3-64 \\  & **LRN** & **conv3-64** & conv3-64 & conv3-64 & conv3-64 \\ \hline \multicolumn{5}{c}{maxpool} \\ \hline conv3-128 & conv3-128 & conv3-128 & conv3-128 & conv3-128 & conv3-128 \\  & **conv3-128** & conv3-128 & conv3-128 & conv3-128 & conv3-128 \\ \hline \multicolumn{5}{c}{maxpool} \\ \hline conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 \\ \hline \end{tabular}
\end{table}
Table 3: VGG-16 and VGG-19 models, based on the original architectures. Added layers have been denoted in bold.  
Figure 5: Xception with entry, middle, and exit flow defined, based on the original architecture.  
During our research into VGG, it was clear that we needed to include at least several different models for our research due to its impressive accuracy and ability to handle challenging image conditions. As such, we settled on the two with the best track record where accuracy and performance are concerned; namely, VGG-16 and VGG-19. Although they are both best-in-class when it comes to image classification, there are several difficulties with the VGG architecture that can make it a challenge to successfully utilize.  
VGG at its core is quite robust; while this can be a boon when training and testing a myriad of image databases, this can also make VGG quite difficult to train without several powerful GPUs. In addition to this, when VGG was first being investigated, some of the weighted that VGG used for calculations caused the models to use an inordinate amount of bandwidth and disk space, although those issues have mostly been mitigated due to recent advances in storage capabilities of computer systems [40].