Video-oculography (VOG) is a tool providing diagnostic information about the progress of the diseases that cause regression of the vergence eye movements, such as Parkinson's disease (PD). The majority of the existing systems are based on sophisticated infra-red (IR) devices. In this study, the authors show that a webcam-based VOG system can provide similar accuracy to that of a head-mounted IR-based VOG system. They also prove that the authors' iris localisation algorithm outperforms current state-of-the-art methods on the popular BioID dataset in terms of accuracy. The proposed system consists of a set of image processing algorithms: face detection, facial features localisation and iris localisation. They have performed examinations on patients suffering from PD using their system and a JAZZ-novo head-mounted device with IR sensor as reference. In the experiments, they have obtained a mean correlation of 0.841 between the results from their method and those from the JAZZ-novo. They have shown that the accuracy of their visual system is similar to the accuracy of IR head-mounted devices. In the future, they plan to extend their experiments to inexpensive high frame rate cameras which can potentially provide more diagnostic parameters.