Robust multimodal 2D and 3D face authentication using local feature fusion

Abstract

In this work, we present a robust face authentication approach merging multiple descriptors and exploiting both 3D and 2D information. First, we correct the heads rotation in 3D by iterative closest point algorithm, followed by an efficient preprocessing phase. Then, we extract different features namely: multi-scale local binary patterns (MSLBP), novel statistical local features (SLF), Gabor wavelets, and scale invariant feature transform (SIFT). The principal component analysis followed by enhanced fisher linear discriminant model is used for dimensionality reduction and classification. Finally, fusion at the score level is carried out using two-class support vector machines. Extensive experiments are conducted on the CASIA 3D faces database. The evaluation of individual descriptors clearly showed the superiority of the proposed SLF features. In addition, applying the ($\hbox {3D} + \hbox {2D}$) multimodal score level fusion, the best result is obtained by combining the SLF with the $\hbox {MSLBP}+\hbox {SIFT}$ descriptor yielding in an equal error rate of 0.98 % and a recognition rate of $\hbox {RR} = 97.22\,\%$.