*4.6. Classification Time*

In this study, we used an EfficientNet with 12 million parameters as the main building block for the fingerprint branch. This model provides impressive results with a low computational cost. In particular, our models converge using only 50 epochs. The complete architecture provides an average classification time for one subject (i.e., fingerprint image + ECG signal) of 30–35 ms (depending on the architecture), which is faster than previous state-of-the-art approaches (i.e., 128 ms [24] and 800 ms [28]). Recall that the approaches described in [21] and [24] applied solutions using only the fingerprint modality and networks with a larger number of weights.
