# Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study
## 4 Discussion
### Results  
From the LFW dataset that was utilized, we had 4788 unique datapoints (images), and 423 labels (individuals). This led to a rather large imbalance in the dataset, which in turn caused a significant decrease in both recall and F1-Scores, the latter of which is reflected in Figure 6a. The accuracy of the dataset, when using an 80/20 testing/training split, can be seen in Figure 6b. A more complete breakdown of the classification report for all tested CNN models can be found in Table 4, which further shows the discrepancies between precision, recall, and F1-Score. We plan to incorporate more balanced datasets in the future to correct these issues. As seen in Table 4, VGG-16 followed by VGG-19 performed best within the group with respect to both F1-Score and overall image classification accuracy. We believe this result is due to the novel way that VGG handles the convolutional layers, though it is a bit surprising that VGG-16 outperformed VGG-19, even though the latter utilized additional convolutional layers. The results we obtained for VGG-16 and VGG-19 are reasonable when compared to other comparative studies of CNN in the literature. Figure 5: Recreated Inception architecture with dimension reductions, based on original image found in [35]. In addition to the typical 80 percent training, 20 percent testing split of the dataset, we have also included the results for both 70/30 and 60/40 training/testing splits. While we will focus most of the discussion on the typical 80/20 split, it is important to also note the results of the other splits that were tested, which can be found in Figures 7 and 8, respectively. In Figure 7, we see the results of changing the testing and training splits to 70% training images, and 30% testing images. Even with a smaller subset of images to train on, we saw a moderate increase in classification report metrics, as well as in overall image accuracy as opposed to the 80/20 training/testing split, as can be observed in Figure 7b. The largest gains made belong to AlexNet and Xception, which both saw increases in the measurable metrics. We postulate that, due to the particular way that both AlexNet and Xception work, the increase in testing images had a more marked effect on those than any of our other tested CNNs. \begin{table}
\begin{tabular}{c c c c c} \hline
**CNN Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline AlexNet & 0.61 & 0.66 & 0.15 & 0.24 \\ \hline Xception & 0.52 & 0.57 & 0.11 & 0.18 \\ \hline Inception v2 & 0.68 & 0.7 & 0.18 & 0.29 \\ \hline Inception v3 & 0.67 & 0.75 & 0.19 & 0.3 \\ \hline ResNet50 & 0.71 & 0.76 & 0.28 & 0.41 \\ \hline ResNet101 & 0.72 & 0.75 & 0.22 & 0.34 \\ \hline VGG16 & 0.84 & 0.84 & 0.36 & 0.5 \\ \hline VGG19 & 0.8 & 0.8 & 0.29 & 0.43 \\ \hline \end{tabular}
\end{table} 
Table 4: Further analysis of Classification Report for our tested CNNs, 80/20 Split. Figure 6: Results of CNN Image Classification testing by **(a)** F1-Score and **(b)** Accuracy, 80/20 Split. Figure 7: Results of CNN Image Classification testing by **(a)** F1-Score and **(b)** Accuracy, 70/30 Split. In Figure 8, we see the results of changing the testing and training splits to 60% training and 40% testing images. As with the previous increase in testing images, most CNNs saw an overall improvement in their classification report metrics and in their overall image accuracy. However, both VGG-16 and VGG-19 saw a slight reduction in the measured metrics. This could be due in part to the reduced amount of training images, which led to a plateau in the overall image classification accuracy for VGG-16 and VGG-19. For AlexNet and Xception, we again saw the best gains of any of our CNN models, which moved their performance from among the worst CNNs that we tested to somewhere in the middle of the pack. In contrast with the VGG models, in our experiments, AlexNet and Xception required less training on a reduced number of images, while still attaining impressive accuracy.