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

IEEE Access

Muhammad Aminu¹  Noor Atinah Ahmad¹ 

[1] School of Mathematical Sciences, Universiti Sains Malaysia, Penang, Malaysia;

关键词: Dimensionality reduction;    face recognition;    manifold learning;    partial least squares;    principal component analysis;    machine learning;   

DOI  :  10.1109/ACCESS.2020.3022784

来源: DOAJ

【 摘 要 】

We propose new appearance-based face recognition methods based on global-local structure-preserving partial least squares discriminant analysis. Two variants of the method are described in this article: 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.

【 授权许可】

Unknown