## 5 Results and Discussion
### Comparison of Results with Other Approaches
#### 5.4.2 Verification  
We compared the results of our text prompted verification task with those delivered by the same approaches described in the identification case. Table 7 presents the comparison of verification results. Regarding the face modality, our bimodal network delivered the best results in all cases with EER values close to or below 3%. Face ResNet delivered results comparable to our proposal while statistical models had considerably greater EER values in each transformation case in comparison to the deep learning models. Standard deviation remained at low values for both deep learning networks and Eigenfaces despite its poor performance. Fisherfaces obtained the worst values in this regard, which is consistent with the results obtained in the identification case. In the speaker verification case our model also achieved the best EER values, equal to or below to 1%. These values are significantly lower in comparison to those obtained by SincNet and X-vectors. Contrary to the identification results, the verification performances of SincNet and X-vectors were more affected by noise, regardless of its SNR value. The Detection Error Tradeoff (DET) plots shown in Figure 2 present a visual comparison of the verification performance delivered by the considered biometric systems presented in this work. The curves were plot by pooling the verification scores of all data augmentation conditions generated by the best iteration of each system. The EER of each biometric system was calculated from their corresponding scores, obtaining the following values: BiMultNet 1.92%, Face ResNet 2.79%, Eigenfaces 25.84%, Fisherfaces 19.73%, Sincnet 7.24%, and Xvectors 2.9%. These results indicate that our proposed bimodal text \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Recognition**} & \multicolumn{5}{c}{**Image Transformations**} \\ \cline{2-6}  & \multicolumn{1}{c}{**No**} & \multirow{2}{*}{**Brightness**} & \multicolumn{1}{c}{**Horizontal**} & \multirow{2}{*}{**Rotation**} & **Combined** \\  & **Transformation** & & & **Flip** & & **Transformations** \\ \hline BiMultNet & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{99.33 \(\pm\) 0.13} & \multirow{2}{*}{95.06 \(\pm\) 0.97} & \multirow{2}{*}{89.53 \(\pm\) 1.93} \\ (Face modality) & & & & & \\ Face ResNet & 98.22 \(\pm\) 1.21 & 84.79 \(\pm\) 2.5 & 96.06 \(\pm\) 0.99 & 98.39 \(\pm\) 1.25 & 98.19 \(\pm\) 1.04 \\ Eigenfaces & 99.93 \(\pm\) 0.13 & 96.53 \(\pm\) 1.81 & 99.33 \(\pm\) 0.13 & 79.53 \(\pm\) 4.87 & 43.06 \(\pm\) 3.86 \\ Fisherfaces & 97.12 \(\pm\) 4.26 & 94.93 \(\pm\) 8.96 & 94.59 \(\pm\) 7.15 & 50.26 \(\pm\) 20.03 & 44.79 \(\pm\) 18.9 \\ \hline \hline \multicolumn{6}{c}{Signal to noise ratio (dB)} \\ \hline  & \multicolumn{1}{c}{Noiseless} & \multicolumn{1}{c}{0} & \multicolumn{1}{c}{5} & \multicolumn{1}{c}{10} & \multicolumn{1}{c}{15} \\ \hline BiMultNet & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{99.93 \(\pm\) 0.13} \\ (Speech modality) & & & & & \\ SincNet & 98.22 \(\pm\) 1.21 & 84.79 \(\pm\) 2.5 & 96.06 \(\pm\) 0.99 & 98.39 \(\pm\) 1.25 & 98.19 \(\pm\) 1.04 \\ X-vectors & 94.04 \(\pm\) 1 & 82.24 \(\pm\) 0.64 & 91.15 \(\pm\) 1.06 & 92.95 \(\pm\) 0.71 & 93.68 \(\pm\) 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results for the identification task in terms of accuracy (%).