The reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency domain, SNR, etc.). The results show that it is possible to identify regions of a face, for which reliable PPG signals can be extracted. The accuracy obtained for the classification task and the mean absolute error achieved for the regression task proved the usefulness of the DNN models.