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The accuracy of functional medical imaging modalities used to assess brain functions are known to be easily degraded by patients’ head movement, due to the extensive duration and the increasing resolution of the scanner. In positron emission tomography these corruptions can cause tracer concentrations to appear blurred or originating from erroneous locations on the final image. Many researches have been using external markers to provide an accurate motion measurement. In this work, we provide a marker-less framework that can track the patient’s 3D head pose during the scan. The framework approaches the problem by combining stereo vision, feature point detection, and the Unscented Kalman Filter. By utilizing features directly available on a patient’s face for tracking, the work eliminates the need of markers common to most current approaches, and therefore effectively minimizes any scanning preparation times and patients’ discomfort. Initial visual inspections show this approach is able to retrieve final transformation parameters matching the extracted feature points with the actual head motion. This framework can be extended to any imaging modality that is affected by patients’ movement.