Multi-linear neighborhood preserving projection for face recognition

Mohamad AL-Shiha, Abeer, Woo, Wai Lok and Dlay, Satnam (2014) Multi-linear neighborhood preserving projection for face recognition. Pattern Recognition, 47 (2). pp. 544-555. ISSN 0031-3203

Full text not available from this repository.
Official URL:


This paper proposes a novel method of supervised and unsupervised multi-linear neighborhood preserving projection (MNPP) for face recognition. Unlike conventional neighborhood preserving projections, the MNPP method operates directly on tensorial data rather than vectors or matrices, and solves problems of tensorial representation for multi-dimensional feature extraction, classification and recognition. As opposed to traditional approaches such as NPP and 2DNPP, which derive only one subspace, multiple interrelated subspaces are obtained in the MNPP method by unfolding the tensor over different tensorial directions. The number of subspaces derived by MNPP is determined by the order of the tensor space. This approach is used for face recognition and biometrical security classification problems involving higher order tensors. The performance of our proposed and existing techniques is analyzed using three benchmark facial datasets ORL, AR, and FERET. The obtained results show that the MNPP outperforms the standard approaches in terms of the error rate.

Item Type: Article
Uncontrolled Keywords: Feature extraction, Multi-linear projection, Neighborhood preserving projection (NPP), Tensor analysis, Face recognition, Dimensionality reduction
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 12 Apr 2019 11:15
Last Modified: 10 Oct 2019 20:04

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics