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In this paper we propose an appearance-based approach to recognition of facial action units (AUs) and their temporal segments in frontal-view face videos. Non-rigid registration using free-form deformations is used to determine motion in the face region of an input video. The extracted motion fields are then used to derive motion histogram descriptors. Per AU, a combination of ensemble learners and hidden Markov models detects the presence of the AU in question and its temporal segment in each frame of an input sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, an average sequence classification rate of 94.3% was achieved.