Among the many known type of intra-class variations, facial expressions are considered particularly challenging, as witnessed by the large number of methods that have been proposed to cope with them. The idea inspiring this work is that dynamic facial features (DFF) extracted from facial expressions while a sentence is pronounced, could possibly represent a salient and inherently safer biometric identifier, due to the greater difficulty in forging a time variable descriptor instead of a static one. We therefore investigated on how a set of geometrical features, defined as distances between landmarks located in the lower half of face, changes across time while a sentence is uttered to find the most effective yet compact representation. The features vectors built upon these time-series were used to train a deep feed-forward neural network on the OuluVS visual-speech database. Testing in identification modality resulted in 98.2% of average recognition accuracy, 0.64% of equal error rate and a remarkable robustness to how the sentence is pronounced.