Facial individuality and expression analysis by eigenspace method based on class features or multiple discriminant analysis

This paper presents two methods for the analysis of facial individuality and expression; an eigenspace method based on class features (EMC) and multiple discriminant analysis (MDA). Those methods are used since they derive eigenvectors by which we may extract facial individuality or expression information from a given facial image. The facial individuality and expression analysis can be achieved by projecting the facial image onto the subspace spanned by a set of those eigenvectors. We apply EMC and MDA to the classification of facial images into 50 classes of individuals or into seven classes of facial expressions, and verify their effectiveness with some experimental results.