Correlation and Interpolation Networks for Real-time Expression Analysis/Synthesis

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Trevor Darrell, Irfan Essa, Alex Pentland


We describe a framework for real-time tracking of facial expressions that uses neurally-inspired correlation and interpolation methods. A distributed view-based representation is used to characterize facial state, and is computed using a replicated correlation network. The ensemble response of the set of view correlation scores is input to a network based interpolation method, which maps perceptual state to motor control states for a simulated 3-D face model. Activation levels of the motor state correspond to muscle activations in an anatomically derived model. By integrating fast and robust 2-D processing with 3-D models, we obtain a system that is able to quickly track and interpret complex facial motions in real-time.