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While it is generally possible to do recognition from video sequences, the training process is usually done over static images. This is due to the fact that, in many applications (e.g., homeland security), one does not have large video sequences which can be used for training. For example, law enforcement agencies generally have a frontal and a profile view of wanted individuals, but do not usually keep video sequences in file. Nonetheless, in these applications, it is still possible to analyze the information of video sequences for subsequent recognition tasks. This paper presents a probabilistic algorithm that learns from small sets of static images and then recognizes faces from video sequences. The proposed algorithm is robust to partial occlusions, different orientations and expression changes and does not require of precise face localizations. Our preliminary results with a small database show that the proposed method is more robust to such changes than static-to-static recognition of faces.