Facial gesture recognition has wide application in Human-Machine Interaction (HMI), which, in the medical area, can be identified with behavioral and emotional analyses, as well as rehabilitative procedures. Although historical approaches for facial expression recognition rely on videos and images data, in recent years, with the progress of the sensors technology and Machine Learning (ML) algorithms, recognition is also being achieved using biological signals, as surface ElectroMyoGraphic (sEMG) signal. The thesis focuses on recognizing and classifying jaw movements and facial expressions from sEMG signals recorded by face muscles during the execution of such actions. The innovative event-driven technique, named Average Threshold Crossing (ATC), is applied to the amplified and filtered sEMG signal to extract the ATC feature.