Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding for Face Recognition

Liang, Jiuzhen; Chen, Chen; Yi, Yaohua; Xu, Xiuxiu; Ding, Meng

doi:10.1109/access.2017.2741223

Cited by 13 publications

(11 citation statements)

References 31 publications

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“…The neighborhood preserving embedding (NPE) method is identical to LPP as both are targeted to discover the local structure of the original data manifold, yet their objective functions are totally different [19]. Linearity makes NPE a fast and suitable technique for practical applications [29][30][31][32][33][34][35][36]. NPE can be performed in either supervised or unsupervised mode.…”

Section: Neighborhood Preserving Embeddingmentioning

confidence: 99%

Weighted Neighborhood Preserving Ensemble Embedding

2019

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Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.

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Section: Neighborhood Preserving Embeddingmentioning

confidence: 99%

Weighted Neighborhood Preserving Ensemble Embedding

2019

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“…J. Liang [46] suggested a new bilateral 2-D neighbourhood preservative discriminant embedding for supervised linear dimensionality decrease for FR. It openly excerpts discriminative face structures from images built on graph embedding plus Fisher's principle.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition Approaches: A Survey

Reddy¹,

Krishna²,

Sumalatha³

2018

IJET

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Face Recognition (FR) is a significant area in computer vision plus pattern recognition. The face is the easiest mode to discriminate the specific individuality of every other. FR is a particular identification scheme that usages particular features of an individual to recognize the individual's identity. The challenges in FR are aged, facial terms, variations in the imaging surroundings, illumination plus posture of the face. Specially, in this study firstly we mark an outline of FR that includes definition, types and problems. Secondly, we provided a complete related work of FR. The objective of this study is to provide a comprehensive outline on the work that has been carried out over the previous spans in the progressing area of FR. This study offers an extensive view of theories, methodologies, up-to-date techniques for FR.

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“…The need for nonlinear dimensionality reduction and representation in appearance-based face recognition has been a well-researched topic. Nonlinear methods such as kernel methods [30], ISOMAP [31]- [33] and maximum variance unfolding (MVU) [34] are developed to preserve the global structure of the face space, while manifold learning methods such as locally linear embedding (LLE) [35], Laplacian eigenmaps [36], locality-preserving projection (LPP) [37]- [39] and neighbourhood-preserving embedding (NPE) [40], [41] attempt to capture local manifold structures. A systematic investigation in [42] revealed that although nonlinear methods have flexibility in learning continuous and smooth data, but inconsistent performance was observed when the data sets have a complicated distribution.…”

Section: Introductionmentioning

confidence: 99%

New Variants of Global-Local Partial Least Squares Discriminant Analysis for Appearance-Based Face Recognition

Aminu

Ahmad

2020

IEEE Access

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We propose new appearance-based face recognition methods based on global-local structurepreserving partial least squares discriminant analysis. Two variants of the method are described in this paper: the neighbourhood-preserving partial least squares discriminant analysis (NPPLS-DA) and the uncorrelated NPPLS-DA (UNNPPLS-DA). In contrast to standard partial least squares discriminant analysis (PLS-DA), which effectively only recognizes the global Euclidean structure of the face space, both NPPLS-DA and UNNPPLS-DA are designed to find an embedding that preserves both the global and local neighbourhood information and obtain a face subspace that best detects the essential manifold structure of the face space. Unlike global-local features extracted using other methods, the global-local PLS-DA features are obtained by maximizing covariance between data matrix and a response matrix which is coded with the class structure of the data. Furthermore, in UNPPLS-DA, an uncorrelated constraint is introduced into the objective function of NPPLS-DA to extract uncorrelated features that are important in many pattern recognition problems. We compare the proposed NPPLS-DA and UNPPLS-DA methods with several competing methods on six different face databases. The experimental results show that the proposed NPPLS-DA and UNPPLS-DA methods provide better representation and consistently achieve higher recognition rates in face recognition than the other competing methods.

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Bilateral Two-Dimensional Neighborhood Preserving Discriminant Embedding for Face Recognition

Cited by 13 publications

References 31 publications

Weighted Neighborhood Preserving Ensemble Embedding

Weighted Neighborhood Preserving Ensemble Embedding

Face Recognition Approaches: A Survey

New Variants of Global-Local Partial Least Squares Discriminant Analysis for Appearance-Based Face Recognition

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