Image-set matching using a geodesic distance and cohort normalization

An image-set based face recognition algorithm is proposed that exploits the full geometrical interpretation of Canonical Correlation Analysis (CCA). CCA maximizes the correlation between two linear subspaces associated with image-sets, where an image-set is assumed to contain multiple images of a person's face. When these linear subspaces are viewed as points on a Grassmann manifold, then geodesic distance on the manifold becomes the natural way to compare image-sets. The proposed method is tested on the ORL data set where it achieves a rank one identification rate of 98.75%. The proposed method is also tested on a subset of the Face Recognition Grand Challenge Experiment 4 data. Specifically, 82 probe and 230 gallery subjects with 32 images per probe and gallery image-set. Our algorithm achieves a rank one identification rate of 87% and a verification rate of 81% at a false accept rate of 1/1;000. These results on FRGC are significantly better than the well-known image-set matching algorithm, Mutual Subspace Method (MSM), which does not use geodesic distance. Another important finding is that cohort normalization boosts verification performance by 50% when used in conjunction with image-set matching. These results suggest that excellent levels of face recognition performance are possible when using image-sets, geodesic distance and cohort normalization. Finally, the proposed approach is generic in the sense that no training is required.