An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition

We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature-based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D Spherical Face Representation (SFR) is used in conjunction with the SIFT descriptor to form a rejection classifier which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach which is robust to facial expressions. This approach automatically segments the eyes-forehead and the nose regions, which are relatively less sensitive to expressions, and matches them separately using a modified ICP algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms which used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74% and 98.31% verification rates at 0.001 FAR and identification rates of 99.02% and 95.37% for probes with neutral and non-neutral expression respectively.