## 3 Results
### Results within Different Visits  
In the previous experiment, both enrolment and recognition data were acquired under the same scenario. Nonetheless, to represent the potential signal variation in a biometric environment, we need to study the results when using different visits for enrolment and recognition. The scheme in Figure 6 represents the steps followed to obtain the different results when using R1 as a train set or enrolment and the remaining visits as a test set or recognition data. It considers a variable training size that refers to the data proportion that is going to be used for training. In the case it is lower than 1, that is, not all the samples are used, we use Stratified Shuffle Split for the division. The variable xD represents the different types of signals, where \(x\) can be changed to refer to ND, FD, or SD. This scheme is followed also by using the remaining visits S, R2, and Ex. Results for all 4 different train sets are represented and discussed in the following paragraphs. For comparison purposes among different graphs, the \(y\)-axis is fixed to be common in all of them. Figure 7 represents all the DET performance curves when R1 is used as the enrolment or train set. The best performance for S is achieved under ND. However, it also provides the worst results of Ex. We can also observe that the results of the three visits are more spread in ND, obtaining very different results depending on the visit. In the cases of FD and SD, the results are more similar among visits, being better in the case of FD as it provides lower FNMR and FMR thro  
Figure 6: Performance evaluation scheme for different test sets when using R1 with \(xD\) signals in training. Similarly, Figure 8 represents DETs for S as training. We observe the same pattern as before, where ND provides more differentiated results among visits, whereas FD and SD have similar trends. The best result is always achieved under R1, which tells that the train and test visits are not highly differentiated. It could be due to the influence of being taken on the same day, or a not relevant variation in the data between sitting and standing. The results for Ex and R2 are not significantly different either because they keep a low EER. In the case of R2 in training, ND keeps performing worse than FD and SD, as seen in Figure 9. However, generally, the three signals have similar performances but reach the best ones under FD, although the improvement is not very noticeable. In these graphs we observe Ex reaching values like those in R1 and S as training, which would not happen in the previous cases. This shows that even R2 and Ex are were taken under different heart rates in the users, the fact that they were close in time results in good performances, obtaining EER around 5%. Figure 8: Results for every test set using (**a**) ND, (**b**) FD, and (**c**) SD signals with 50% of S as training. Figure 7: Results for every test set using (**a**) ND, (**b**) FD, and (**c**) SD signals with 50% of R1 as training. Finally, Ex is used as training, collecting its results in Figure 10. As expected, due to the higher heart rate in this visit, results do not improve the ones seen so far. However, it is consistent with FD being the best signal in performance. In addition, results for R2 are the lowest ones, reinforcing the theory that the signal taken in the same day behaves well, despite the different heart rate. Again, R1 and S have similar trends. When using the same data set in training and testing, we observed that FD achieved better results, and it has been consistent when testing with different visits. Even though best results are obtained with R1 as train set, keeping the EER around 5% or lower when using FD, we have observed that similar results have also been obtained using FD when R2 is the train set. This provides two important pieces of information: resting is the most suitable condition for the user to be enrolled with, and the system still provides good results when identifying with information taken on different days.