Compensating for Ensemble-Specificity Effects when Building Facial Models

When attempting to code faces for modeling or recognition, estimates of dimensions are typically obtained from an ensemble. These tend to be significantly sub-optimal. Firstly, ensembles are rarely balanced with regard to identity and expression. This can be overcome by dividing the ensemble by type of variation and rotating sub-spaces relative to one another. Secondly, each face contains both predictable and non-predictable qualities; only the predictable aspects are useful for defining coding systems for other faces. Variance-based methods of defining codes (PCA) will provide eigenvectors which are themselves potential faces. Predictable aspects will induce eigenvectors with comparable levels of spatial redundancy to the ensemble. We show that this gives relatively short and consistent codes, and allows fast and accurate fitting of codes to faces.