Multimodal 2d + 3d multi-descriptor tensor for face verification Adel Saoud 1 & Abdelmalik Oumane 1 & Abdelkrim Ouafi 1 & Abdelmalik Taleb-Ahmed 2 Received: 9 June 2019 /Revised: 15 March 2020 /Accepted: 22 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this paper, we proposed a multi- modal 2D + 3D face verification system based on Multilinear Discriminant Analysis MDA integrating Within Class Covariance Normalization WCCN technique. Histograms of local descriptor applied to features extraction from 2D and 3D face images are concatenated and organized as a tensor design. This tensor is then reduced and projected using MDA technique into a lower subspace. WCCN technique is used to reduce the effect of the intra class directions using normalisation transform and to enhance the discrimination power of the MDA. Our experiments were carried out on the three biggest databases: FRGC v2.0, Bosphorus and CASIA 3D under expressions, occlusions and pose variations. Experimental results showed the superiority of the proposed approach in term of verification rate when compared to the state of the art method. Keywords Face verification . Multilinear principal component analysis (MPCA) . Multilinear discriminant analysis (MDA) . Dimensionality reduction . Subspace tensor . Fusion 2D-3D modalities Multimedia Tools and Applications https://doi.org/10.1007/s11042-020-09095-y * Adel Saoud adel.saoud2013@gmail.com Abdelmalik Oumane ouamaneabdealmalik@yahoo.fr Abdelkrim Ouafi ou_karim@yahoo.fr Abdelmalik Taleb-Ahmed abdelmalik.taleb-ahmed@univ-valenciennes.fr 1 University of Biskra, Biskra, Algeria 2 University of Valenciennes, Valenciennes, France