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Recent studies have shown the success of face recognition using low resolution prosthetic vision, but it requires a zoomed-in and stably-fixated view, which will be challenging for a user with the limited resolution of current prosthetic vision devices. We propose a real-time object detection and tracking system capable of fixating human faces. By integrating both static and temporal information, we are able to improve the robustness of face localization so that it can fixate on faces with large pose variations. Our qualitative and quantitative results demonstrate the viability of supplementing visual prosthetic devices with the ability to visually fixate objects automatically, and provide a stable zoomed-in image stream to facilitate face and expression recognition.