Learning local representations for scalable RGB-D face recognition

In this article we present a novel RGB-D learned local representations for face recognition based on facial patch description and matching. The major contribution of the proposed approach is an efficient learning and combination of data-driven descriptors to characterize local patches extracted around image reference points. We explored the complementarity between both of deep learning and statistical image features as data-driven descriptors. In addition, we proposed an efficient high-level fusion scheme based on a sparse representation algorithm to leverage the complementarity between image and depth modalities and also the used data-driven features. Our approach was extensively evaluated on four well-known benchmarks to prove its robustness against known challenges in the case of face recognition. The obtained experimental results are competitive with the state-of-the-art methods while providing a scalable and adaptive RGB-D face recognition method. (c) 2020 Elsevier Ltd. All rights reserved.