Active appearance model search using partial least squares regression

A novel active appearance model (AAM) search algorithm based on partial least squares (PLS) regression is proposed. PLS models the relationship between independent (texture residuals) and dependent (error in the model parameters) variables in the training phase by extracting from independent and dependent variables a set of orthogonal factors called latent variables respectively which have the maximum covariance. During search, the parameter updates with the best predictive power are extracted from the texture residuals. On the other hand, PLS is well suited for the low observation-to-variable ratio context, where the sample covariance matrix is likely to be singular, which is very common in AAM. Experiments show that the proposed method has better performance than the original AAM and comparable performance to AAM search based on Canonical correlation analysis (CCA-AAM) in terms of convergence speed, whilst affording superior computational efficiency.