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Multi-linear neighborhood preserving projection for face recognition

Lookup NU author(s): Dr Wai Lok Woo, Professor Satnam Dlay


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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.

Publication metadata

Author(s): Abeer AMA, Woo WL, Dlay SS

Publication type: Article

Publication status: Published

Journal: Pattern Recognition

Year: 2013

Volume: 47

Issue: 2

Pages: 544-555

Print publication date: 26/08/2013

ISSN (print): 0031-3203

ISSN (electronic): 1873-5142

Publisher: Elsevier Ltd.


DOI: 10.1016/j.patcog.2013.08.005


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