Multi-linear neighborhood preserving projection for face recognition |
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| Affiliation: | 1. RECOD Lab, Institute of Computing (IC), University of Campinas (Unicamp) – Av. Albert Einstein, 1251, Campinas 13083-852, SP, Brazil;2. Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp) – Av. Albert Einstein, 400, Campinas 13083-852, SP, Brazil;3. Paris-Est University, IGN/SR, MATIS Lab, 73 avenue de Paris, 94160 Saint-Mandé, France;4. CNAM, CEDRIC Lab, 292 rue Saint-Martin, 75141 Paris Cedex 03, France;1. Department of Electrical and Computer Engineering, Duke University, USA;2. Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Uruguay;3. CNRS - LTCI UMR5141, Telecom ParisTech, France;4. Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina;1. University of Bristol, Bristol, UK;2. University of Bath, Bath, UK;2. XLIM-SIC UMR CNRS 6172, Signals, Images and Communications Laboratory, University of Poitiers, France;1. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, China;2. Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China;3. Faculty of Science of Technology, University of Macau, China |
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| Abstract: | 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. |
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| Keywords: | Feature extraction Multi-linear projection Neighborhood preserving projection (NPP) Tensor analysis Face recognition Dimensionality reduction |
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