Iconic Methods for Multimodal Face Recognition: A Comparative Study

When dealing with face recognition, multimodal algorithms, with their potential to capture complementary characteristics from the 2D and 3D data channels, can reach high level of efficiency and robustness. In this paper, we explore different combinations of iconic descriptors coupled with a shape descriptor and propose a fully automatic, multimodal, face recognition paradigm. Two iconic features extractors, the Scale Invariant Feature Transform (SIFT) and the Speeded-Up Robust Features (SURF), are used, in turn, to extract salient points from the images of the faces. The corresponding points on the scans are validated with Joint Differential Invariants, a 3D characterisation method based on local and global shape information. SIFT and SURF are then combined at feature level and the 3D Joint Differential Invariants used to validate them on the shape channel. The proposed method has been tested on the FRGCv2 database. Experimental results highlight the complementarity of the feature points extracted by SIFT and SURF and the effectiveness of their 3D validation.