# Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study
## 3 Methodology
### ResNet50/ResNet101  
As the task of image classification and recognition accuracy continues to become more complex, there is a need to create deeper and deeper neural networks to handle those  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{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{6}{c}{**input (224 \(\times\) 224 RGB image)**} \\ \hline conv3-64 & conv3-64 & conv3-64 & conv3-64 & conv3-64 & conv3-64 & conv3-64 \\  & **LRN** & **conv3-64** & conv3-64 & conv3-64 & conv3-64 \\ \hline \multicolumn{6}{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{6}{c}{maxpool} \\ \hline conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 \\ conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 & conv3-256 \\  & & & **conv1-256** & **conv3-256** & conv3-256 \\  & & & & **conv3-256** \\ \hline \multicolumn{6}{c}{maxpool} \\ \hline conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\ conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\  & & & **conv1-512** & **conv3-512** & conv3-512 \\ \hline \multicolumn{6}{c}{maxpool} \\ \hline conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\ conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\  & & & **conv3-512** & **conv3-512** \\ \hline \multicolumn{6}{c}{maxpool} \\ \hline conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\ conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 & conv3-512 \\  & & & **conv1-512** & **conv3-512** & conv3-512 \\ \hline \multicolumn{6}{c}{maxpool} \\ \hline \multicolumn{6}{c}{FC-4096} \\ \hline \multicolumn{6}{c}{FC-1000} \\ \hline \multicolumn{6}{c}{soft-max} \\ \hline \hline \end{tabular}
\end{table}
Table 2: VGG recreation, based on the original image found in [31].  
challenges. Unfortunately, as additional layers are added, the difficulty in training those neural networks lead to a degradation in their accuracy. The specific architecture of ResNet was created to help solve this problem.  
Introduced in 2015 by [PERSON], [PERSON], [PERSON], and [PERSON], Residual Network (ResNet) is a type of CNN that is able to stack additional layers and attain increased performance and accuracy [32]. The layers that are added are able to learn more and more complex features, which in turn correlates to better system performance overall, as well as markedly increased image classification accuracy. This increase in layers needs to be balanced, as adding too many layers can cause an increase in error percentage as compared to a ResNet with fewer layers. An example of ResNet architecture can be found in Table 3[32].  
In our research, we determined that ResNet50 and ResNet101 were ideal candidates to be included in our study of CNNs. ResNet50 could be considered the standard version of ResNet, and has seen great success in the realm of image classification. ResNet101 has gone up against VGG-16 in several tests, and has seen impressive results, sometimes besting that particular CNN [33].