As opposed to still-image based paradigms, video-based face recognition involves identifying a person from a video input. In video-based approaches, face detection and tracking are performed together with recognition, as usually the background is included in the video and the person could be moving or being captured unknowingly. By detecting and raster-scanning a face sub-image to be a vector, we can concatenate all extracted vectors to form an image set, thus allowing the application of face recognition algorithms based on matching image sets. It has been reported that linear subspace-based methods for face recognition using image sets achieve good recognition results. The challenge that remains is to update the linear subspace representation and perform recognition on-the-fly so that the recognition-from-video objective is not defeated. Here, we demonstrate how this can be achieved by using a well-studied incremental SVD updating procedure. We then present our online face recognition-from-video framework and the recognition results obtained.