Facial action c technique

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.

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

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.