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Video retrieval in consumer applications demands high level semantic descriptors such as people's identity. The problem is that in a variety of videos such as home videos, Hollywood content, TV broadcast content; mobile phone videos faces are not easy to recognize. Even more, a closed system trained to recognize only a predetermined number of faces become obsolete very easily. We developed an online-learning face recognition system for a variety of videos based on modified probabilistic neural networks (MPNN). This face recognition system can detect and recognize known faces, as well as automatically detect unknown faces and train the unknown faces online into new face classifiers such that this "unknown face" can be recognized if it appears again. MPNN is a variant of the PNN with thresholding on the category (output) layer of a probabilistic neural network (PNN) in order to detect unknown categories of input data. The PNN training makes the online training very fast because adding new faces does not require retraining of the known categories. Our experimental results show that on-line learning gives somewhat lower hit rate, while at the same time reducing the false positive rate.