This paper addresses semi-automatic annotation of faces in personal videos. Different from previous offline annotation systems, this paper studies online annotation of faces. During an annotation session, few annotations are requested from the user only for some part of the video online. These annotations are used to train a system that will perform annotation automatically for the rest of the video. The automatic annotation results are presented to the user during the same session and the user is allowed to correct any automatic annotation mistakes. Thus, only fast and accurate face recognition methods are considered. Instead of batch learning, which has been used in the existing annotation systems, this paper proposes sequential learning methods to be used as face classifiers. Different classification methods are tested with various feature extraction methods using the same database so that a fair comparison is made among them. The results are evaluated in terms of recognition accuracies and execution time requirements.