Abstract
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.
Highlights
In appearance-based face recognition, a facial image is considered a vector of pixels and is represented as a single point in the high-dimensional space
The experiments are designed to compare the performances of the proposed NPPLS-DA and UNPPLS-DA methods with the existing methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), neighbourhood-preserving embedding (NPE) and the more recent robust sparse linear discriminant analysis (RSLDA) [10]
Since all the databases used in our experiments have a substantially large amount of individuals (C), we set δ > 0 in all our previous experiments and tune it to achieve the best performance for the UNPPLS-DA method
Summary
In appearance-based face recognition, a facial image is considered a vector of pixels and is represented as a single point in the high-dimensional space. As the number of images in the data set increases, the complexity of classification and discrimination increase. It was pointed out in [1] that the difficulty in high-dimensional classification is intrinsically caused by the existence of many noisy features in the data that do not contribute to the reduction of the misclassification rate. Dimensionality reduction and feature selection techniques are employed prior to classification to extract features that contain most of the necessary information in the data. The directions determined by these methods assign more weights to the features that contribute the most to the classification of the data. PCA is an unsupervised dimension reduction technique that captures most of the variance of a data, while LDA is a supervised dimension reduction technique that aims at discriminating the different classes in the data
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