  Face recognition systems are present in many modern solutions and thousands
of applications in our daily lives. However, current solutions are not easily
scalable, especially when it comes to the addition of new targeted people. We
propose a cluster-matching-based approach for face recognition in video. In our
approach, we use unsupervised learning to cluster the faces present in both the
dataset and targeted videos selected for face recognition. Moreover, we design
a cluster matching heuristic to associate clusters in both sets that is also
capable of identifying when a face belongs to a non-registered person. Our
method has achieved a recall of 99.435% and a precision of 99.131% in the task
of video face recognition. Besides performing face recognition, it can also be
used to determine the video segments where each person is present.
