## Materials and Methods
### Performance Measurement  
To evaluate the performance of a verification system, the ISO 19795 [36] does not encourage the use of single values such as percentages, because they miss relevant information. The Detection Error Trade-off (DET) graphs are the main tool instead, as they show the False Non-Match Rate (FNMR) and False Match Rate (FMR) evolution with respect to different thresholds. The point where both are equal represents the lower possible error rate and is called the Equal Error Rate (EER) [5].  
The decision on whether the user is whom he/she says is based on the scores retrieved by the classifier. This implies that depending on the applied criteria, the system could not verify genuine users (FNMR) when doing otherwise with impostors (FMR). Some biometric applications, such as the entrance of a gym, would rather have higher FMR as a trade-off for a fast authentication process. On the other hand, if the authentication is for a bank account, the system would rather have high FNMR. This could increase the probability of asking the user for their data more than once, making the process more inconvenient. The trade-off between convenience and security must be determined in design, depending on the system's target application.  
In the case of the selected database, classes for the test samples are known. With this information, the model's performance can be assessed from the probabilities retrieved by the MLP classifier. Typically, scorers or loss functions are applied for this purpose. However, in this case, we implement a different approach and apply a custom function that implements the EER calculation.  
To do so, when several samples from the same class are used in the test set, all the probabilities are unified applying the mean. This provides only one probability vector for that given class in the test, containing the mean probability for every comparison combination. By paying attention to the reference class in every comparison, we can divide these results into two types: mated that is, when reference and comparison classes are the same; and non-mated if otherwise. From them, we can  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Description** & **Value** \\ \hline Hidden layers & Number of hidden layers. & 1 \\ \hline Solver & Function used for weight updating. & adam \\ \hline \multirow{2}{*}{Learning rate} & Function used to update the learning rate & Not applicable \\  & that takes part in the solver. & with adam solver. \\ \hline Learning rate step & Step size for the learning rate updates. & 0.0001 \\ \hline No change iterations & Number of iterations with no relevant & 10 \\  & change to consider convergence. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summarization of the fixed MLP hyperparameters.  
obtain the False FNMR and FMR. As they represent both possible types of error, the crossing point of both lines gives the EER, acting as the score for further optimization.  
Despite ISO guidelines, performance metrics are still heterogeneous in the state-of-the-art. To be able to compare this work's results with similar ones in the literature, accuracy calculation was also implemented. In this case, a threshold is applied to the scores retrieved by the classifier, giving values 0 (if discarded) or 1 (if verified). The accuracy is calculated by doing a simple division of total correct classifications by the total number of samples.