# Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study
## 4 Discussion
### Results
While both VGG-16 and VGG-19 showed impressive accuracy in all the tested configurations, they were not without their drawbacks. Although we are not currently factoring in processing time in our results, it should be noted that both VGG-16 and VGG-19 performed poorly with respect to those metrics, which can be viewed in detail in Table 5. Further testing will be conducted in the future to determine how much of an effect those two particular metrics may have on our results. We have included here a brief example of correctly and incorrectly classified images with respect to some of the CNN architectures that were tested. While the models did reach a consensus on some of the images, several images were correctly classified by some of the models, and incorrectly classified by others. As can be viewed in Figure 9, there was sometimes a rather significant discrepancy, even between similar models (such as VGG-16 and VGG-19). Figure 8: Results of CNN Image Classification testing by (**a**) F1-Score and (**b**) Accuracy, 60/40 Split. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CNN Model** &
\begin{tabular}{c} **Trainable** \\ **Params** \\ \end{tabular} & **80/20 Split** & **70/30 Split** & **60/40 Split** \\ \hline AlexNet & 62,378,344 & 3 min 25 s & 3 min 41 s & 4 min 05 s \\ \hline Xception & 22,855,952 & 3 min 54 s & 4 min 07 s & 4 min 19 s \\ \hline Inception v2 & 55,813,192 & 3 min 11 s & 3 min 51 s & 4 min 39 s \\ \hline Inception v3 & 23,817,352 & 2 min 41 s & 2 min 58 s & 3 min 24 s \\ \hline ResNet50 & 25,583,592 & 2 min 56 s & 3 min 32 s & 3 min 39 s \\ \hline ResNet101 & 44,601,832 & 3 min 18 s & 3 min 41 s & 3 min 56 s \\ \hline VGG16 & 138,357,544 & 7 min 33 s & 8 min 39 s & 10 min 24 s \\ \hline VGG19 & 143,667,240 & 8 min 01 s & 9 min 41 s & 11 min 55 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Computational Time between CNNs. Moving clockwise from the top left image, we can see that both VGG models correctly identified the individual, yet there was a discrepancy between ResNet50 and ResNet101's conclusions. For the image at the top right, again ResNet50 and ResNet101 reached different conclusions, as did VGG16 and VGG19. The image at the bottom right found VGG16 and VGG19 again at odds, along with Inception v2 and Inception v3, which incorrectly and correctly identified the images, respectively. Finally, the image at the bottom left was correctly identified by seven of the eight models, with AlexNet being the only outlier. It is interesting to see different conclusions being reached even among similar CNN models. In the case of ResNet50 and ResNet101, this discrepancy is likely due to the disparate number of trainable parameters between the two models; as for VGG16 and VGG19, the three extra convolutional layers in VGG19 led to that model finding different results from VGG16 in the top left and bottom left images. In addition, while both models of the Inception architecture found a consensus with three of the images in Figure 9, the disagreement caused by the bottom-left image could be due to the widening gap between their number of trainable parameters, or other minor differences between the two versions of Inception that we utilized.