This paper presents a new approach for face and kinship verification under unconstrained environments. The proposed approach is based on high order tensor representation of face images. The face tensor is built based on local descriptors extracted at multiscales. Besides, we formulate a novel Multilinear Side-Information based Discriminant Analysis (MSIDA) to handle the weakly supervised multilinear subspace projection and classification. Using only the weak label information, MSIDA projects the input face tensor in a new subspace in which the discrimination is improved and the dimension of each tensor mode is reduced simultaneously. Experimental evaluation on four challenging face databases (LFW, Cornell KinFace, UB KinFace and TSKinface) demonstrates that the proposed approach significantly outperforms the current state of the art.
Bessaoudi Mohcene, Ouamane Abdelmalik, Belahcene Mebarka, Chouchane Ammar, Boutellaa Elhocine, Bourennane Salah
A1 Journal article – refereed
Place of publication:
Mohcene Bessaoudi, Abdelmalik Ouamane, Mebarka Belahcene, Ammar Chouchane, Elhocine Boutellaa, Salah Bourennane, Multilinear Side-Information based Discriminant Analysis for face and kinship verification in the wild, Neurocomputing, Volume 329, 2019, Pages 267-278, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2018.09.051
Read the publication here: