Facial action c technique

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It appears that high spatial frequencies are important for this task. The results obtained here, however, were comparable to the performance of other facial expression recognition systems based on optic flow [ 64 ], [ 54 ]. Section 4 examines a representation of facial motion based on optic flow. Details are available in [ She is presently exploring probabilistic dynamical models and their application to video analysis at the University of California, San Diego. Neural Computation; La Jolla, Calif: Representing Face Images for Emotion Classification.

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Naive subjects Naive subjects were 10 adult volunteers with no prior knowledge of facial expression measurement.

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Classifying Facial Actions

Second-order dependencies are pixelwise covariances. The variance in the assigned feature location using this procedure was 0. Many of these approaches employ data-driven kernels learned from the statistics of the face image ensemble. Expert subjects were not given a guide sheet or additional training and the complete face was visible, as it would normally be during FACS scoring. Essa and Pentland [ 26 ] extended this approach, using optic flow to estimate activity in a detailed anatomical and physical model of the face. The basis images that the algorithm produced, however, were local. His thesis work dealt with the recognition of facial actions.

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