Abstract
We investigate the ability of Curvelet transform to extract robust features from 3D faces under different facial expressions and pose variations. We also investigate the contribution of fusing 3D and 2D data to achieve multimodal face identification using Curvelet features to describe semi-rigid regions. Extracted Curvelet features are filtered before applying PCA for dimensionality reduction. In the testing phase, a similarity matrix is built by matching faces from probe and gallery. Four different matching processes (with respect to the type of data and the regions of interest) were considered. The resulting similarity matrices were fused at the score level to generate the proposed Curvelet-based multimodal face identification system. Our system was evaluated using the well-known and challenging FRGC v2 dataset. Reported experimental results show that the proposed Curvelet-based approach provides superior performance for non-neutral facial expressions, with an identification rate of 93.74%. For neutral expressions, the system is shown to exhibit an identification rate of 97.43%.