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Using Dynamic 3D Facial Data to Create Appearance Models

We then perform a Principal Component Analysis on the training data matrix and obtain a set of Eigen vectors which represent the different Modes of Variations of the facial dynamics. We usually reorder the Eigen vectors by intensity. Considering that 98% of the variations are usually contained in the first 10 modes, we can reduce the high dimensionality of our model and improve at the same time the processing cost.