# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Experiments and Results
#### 4.2.4 Performance Analysis  
The performance analysis of all experiments was based on the most common evaluation measures used for statistical tests, such as accuracy, precision, recall, and f_measure. The accuracy was the fraction of the predicted labels that were correct. Accuracy recall was defined as:  
\[\text{Accuracy}\ =\ \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{2}\]  
where TP is the true positives rate, TN is the true negatives rate, FN is false negatives, and FP is false positives.  
Figure 11: Recognition accuracy for transfer learning from AlexNet model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **FEI Face** & **ORL** & \begin{tabular}{c} **Georgia** \\ **Tech Face** \\ \end{tabular} & \begin{tabular}{c} **GTAV** \\ **Face** \\ \end{tabular} & \begin{tabular}{c} **LFW** \\ **F\_LFW** \\ \end{tabular} & \
\begin{tabular}{c} **F\_LFW** \\ \end{tabular} &
\begin{tabular}{c} **YTF** \\ \end{tabular} \\ \hline AlexNet + SVM & 97.50\% & 99.17\% & 96\% & 99.55\% & 94\% & 98\% & 100\% \\ Transfer learning (AlexNet) & 98.70\% & 99.17\% & 100\% & 100\% & 95.63\% & 99.3\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results with AlexNet with SVM and transfer learning on AlexNet.  
Figure 10: Accuracy for CNN models.  
Recall represented the fact that the fraction is of true positive instances to the sum of true positives and false negatives. Recall was defined as:  
\[\text{Recall}=\ \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
Precision represented fraction of true positive instances to all positive instances. Precision was defined as:  
\[\text{Precision}=\ \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
F_Measure represented the combination of precision and recall. F_Measure was defined as:  
\[\text{F\_measure}\ =\ \frac{(\text{precision}\ *\ \text{recall})}{(\text{ precision}\ +\ \text{recall})} \tag{5}\]  
The performance analysis of all the approaches (AlexNet with SVM, ResNet-50 with SVM, and transfer learning from AlexNet) based on accuracy, precision, recall, and F_Measure with all datasets (FEI, ORL, Georgia Tech face, GTAV face, LFW, F_LFW, and YTF) shown in Figure 12. The performance analysis for all models was as follows:  
First, in terms of accuracy, as shown in Figure 12a, we can observe that the AlexNet + SVM model obtained the highest results with an accuracy of 100% on the YTF dataset. Also, pre-trained CNN ResNet-50 + SVM achieved an accuracy of 100% on GTAV face dataset, ORL dataset, and YTF dataset. When using transfer learning from the AlexNet model, we can observe the higher accuracy of 100% being achieved on the Georgia Tech face dataset, GTAV face dataset, and YouTube face dataset. Also, we compared the results when testing the models on the DB_Collection dataset that included a combination of images from all datasets. We can observe from Figure 12a the accuracy was 97% with the AlexNet + SVM model, 97.50% with the ResNet-50 + SVM model, and 98.32% with the transfer learning from the AlexNet model.  
Second, the results for precision value is illustrated in Figure 12b. The measure value of precision for AlexNet + SVM was in the range of 92%-99% with all datasets. For the approach ResNet-50  
Figure 12: Performance analysis for approaches with all datasets: (**a**) Accuracy, (**b**) Precision, (**c**) Recall, (**d**) F_Measure.  
Figure 13: Testing time for convolutional neural network models.