Open AccessHead pose estimation and face recognition using a non‐linear tensor‐based model
Author(s) -
Takallou Hadis Mohseni,
Kasaei Shohreh
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0217
Subject(s) - artificial intelligence , computer science , facial recognition system , face (sociological concept) , pose , pattern recognition (psychology) , nonlinear dimensionality reduction , computer vision , context (archaeology) , tensor (intrinsic definition) , manifold (fluid mechanics) , mathematics , dimensionality reduction , social science , sociology , pure mathematics , mechanical engineering , paleontology , engineering , biology
Although the ability to estimate the face pose and recognise its identity are common human abilities, they are still a challenge in computer vision context. In this study, the authors aim to overcome these difficulties by learning a non‐linear tensor‐based model based on multi‐linear decomposition. Proposed model maps the high‐dimensional image space into low‐dimensional pose manifold. For preserving the actual distance along the manifold shape, a graph‐based distance measure is proposed. Also, to compensate for the limited number of training poses, mirrored images are added to training ones to improve the recognition accuracy. For performance evaluation of the proposed method, experiments are run on three famous face databases using three different manifold shapes and two different distance measures. Eight training data modes are chosen such that the influential parameters are studied comprehensively. The obtained results confirm the effectiveness of proposed model in achieving high accuracy in pose estimation and multi‐view face recognition, even with different training poses for different identities.