Hypomimia, the reduction of spontaneous facial movements, is an early and disabling symptom of Parkinson’s disease (PD). Its clinical evaluation is currently subjective and based on qualitative rating scales. Gold standard (GS) methods including manual visual expert inspection and surface electromyography (EMG) provide objective assessments but require expensive equipment, trained personnel, and may interfere with natural facial expressivity. Recently explored markerless (ML) methods using RGB and RGB-D cameras, combined with deep learning-based facial landmark detection techniques, represent a non-invasive alternative. However, their clinical validation remains debated. This thesis aimed to (i) propose a low-cost ML method using a single RGB-D camera to quantify facial muscle activity, (ii) validate it against GS measures (manual measurements and EMG signals), (iii) assess the impact of the depth sensor and RGB image resolution on its performance and (iv) evaluate its applicability in discriminating between young healthy (YH), elderly healthy (EH), and PD subjects during emotions.