He, Xuming; Kim, Junae; Barnes, Nick
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...[Show more]
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