*4.1. Experiments Using Fingerprint Modality Only*

Initially, we examined the performance of the proposed fingerprint network based on EfficientNet. This evaluation allows us to compare the performances of this network in terms of fingerprint PAD with that of the methods proposed in the LivDet 2015 competition [27]. Table 4 shows the results after training the network for 50 iterations.

**Table 4.** Comparison between the results of the proposed fingerprint branch net and the best methods from the LivDet 2015 competition, where we present the average accuracy %.


We can see from the results in Table 4 that the proposed fingerprint network achieves an overall classification accuracy of 94.87%. A comparison of the reported accuracy of the proposed network with those reported from the LivDet 2015 competition shows that our method would have been the second-best approach. Moreover, the proposed method follows the same behavior as the other two algorithms in terms of its accuracy for the individual sensors, achieving a high accuracy of 97.29% for the Crossmatch sensor (i.e., an easy to learn sensor) and a relatively lower accuracy of 91.96% for the Digital Persona sensor (i.e., a difficult to learn sensor). Furthermore, the proposed method achieves moderately high accuracies of 94.68% and 95.12% for the Green bit and Biometrika sensors, respectively. Figure 11 shows the progress of the loss function during the training on the LivDet 2015 dataset (training part). Note that the loss converges at a low number of iterations (nearly 25 iterations). The reported results confirm the promising capability of the network in detecting PAs, motivating us to improve it further by proposing a multimodal solution that fuses fingerprints with ECG signals.

**Figure 11.** Model loss versus number of epochs (50) by training on LivDet 2015 dataset.
