The hypomimia is a main clinical sign of Parkinson disease that describes motor patterns associated with the reduction and progressive loss of facial expression. This clinical sign constitutes a main biomarker to support diagnosis, even at early stages, and to establish progression and description of the disease. In clinical routine, the evaluation of such signs remains subjective or limited to the description of some landmarks that poorly describe little expressions correlated with the disease. This work introduces a new digital biomarker, expressed as a spatio-temporal convolutional representation that learns facial movement patterns to discriminate between Parkinson and control patients. The proposed architecture builds a representation through 3D convolutional layers, which are integrated from inception modules, achieving salient maps of face expression activations. This approach was validated in a retrospective study that includes 16 Parkinson patients and 16 control subjects. The architecture achieves an average accuracy of 91.87% using 480 video sequences in classification condition task. Clinical relevance- A digital descriptor that quantify ges-tural face signatures described from a deep spatio-temporal representation with the capability to discriminate Parkinsonian patients.