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Presents work on a project for the automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how spontaneous speech differs from directed speech. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since people often nod or turn their heads, automatic recognition of spontaneous facial behavior requires methods for handling out-of-image-plane head rotations. There are many promising approaches to address the problem of out-of-image plane rotations. In this paper, we explore an approach based on 3D warping of images into canonical views. A front-end system was developed that jointly estimates camera parameters, head geometry and 3D head pose across entire sequences of video images. First, a small set of images was used to estimate camera parameters and 3D face geometry. Markov-chain Monte-Carlo methods were then used to recover the most likely sequence of 3D poses given a sequence of video images. Once the 3D pose was known, we warped each image into frontal views with a canonical face geometry. We evaluated the performance of the approach as a front-end for a spontaneous expression recognition system using support vector machines and hidden Markov models. This system employed general-purpose learning mechanisms that can be applied to recognition of any facial movement. We showed that 3D tracking and warping, followed by machine learning techniques directly applied to the warped images, is a viable and promising technology for automatic facial expression recognition. One exciting aspect of the approach presented is that information about movement dynamics emerged out of filters which were derived from the statistics of images.