Open AccessMultiview face recognition based on multilinear decomposition and pose manifold
Author(s) -
Takallou Hadis Mohseni,
Kasaei Shohreh
Publication year - 2014
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0003
Subject(s) - multilinear map , artificial intelligence , subspace topology , pose , facial recognition system , face (sociological concept) , computer science , pattern recognition (psychology) , manifold (fluid mechanics) , computer vision , mathematics , mechanical engineering , social science , sociology , pure mathematics , engineering
One major challenge encountered in face recognition is how to handle the wide pose variation and in‐depth rotations of head. A multiview face recognition method is proposed in this study that addresses this challenge based on multilinear decomposition approach and pose subspace. In order to preserve the pose manifold geometry among different individuals in pose subspace, a pose‐biased distance measure is proposed. In addition, as one of the impediments in manifold‐based methods is the lack of sufficient data, a new half‐ellipsoid‐based pose generation method is presented. For performance evaluation of the proposed multiview face recognition method, three different experiments are run on three famous face datasets. The obtained recognition accuracy and the cumulative match characteristic curves confirm the effectiveness of the proposed method in wide pose variation, even with limited number of training poses.