Estimating 3D facial shape and motion from stereo image using active appearance models with stereo constraints

Abstract

This paper proposes a new fitting algorithm which we call Stereo Active Appearance Model (STAAM). This algorithm fits a 2D+3D Active Appearance Model to stereo images acquired from calibrated vision system and computes the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. Moreover, the STAAM uses a modified inverse compositional simultaneous update fitting algorithm to reduce the fitting computation greatly. Experimental results show that (1) the modified inverse compositional simultaneous update algorithm accelerates the AAM fitting speed while keeping its fitting accuracy, (2) the STAAM improves fitting stability using calibration information.