Local Gabor features (jets) have been widely used in face
recognition systems. Once the sets of jets have been extracted from the two faces to be compared, a proper measure of similarity (or distance) between corresponding features should be chosen. For instance, in the well known Elastic Bunch Graph Matching (EBGM) approach and other Gabor-based face recognition systems, the cosine distance was used as a measure. In this paper, we provide an empirical evaluation of seven distance measures for comparison, using a recently introduced face recognition system, based on Shape Driven Gabor Jets (SDGJ). Moreover we evaluate different normalization factors that are used to pre-process the jets. Experimental results on the BANCA database suggest that the concrete
type of normalization applied to jets is a critical factor, and that
some combinations of normalization + distance achieve better performance
than the classical cosine measure for jet comparison.