Abstract
Graph‐based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra‐class variations of the face images. Hence, features which are extracted by the graph‐based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra‐class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.
Highlights
A face image with size m × n can be perceived as a vector in an image space Rm×n
We propose to decompose the whole eigenspace, constituted by all the eigenvectors computed through 1.1, of subspace learning approach into three subspaces: a subspace due to facial intraclass variations noise I subspace, N-I, an intrinsic face subspace face subspace, F, and a subspace that is attributed to sensor and external noises noise II subspace, N-II
The FRGC data was collected at the University of Notre Dame 12
Summary
A face image with size m × n can be perceived as a vector in an image space Rm×n. Discrete Dynamics in Nature and Society and Linear Discriminant Analysis LDA 3 ; and they have demonstrated a fairly good performance in face recognition These algorithms assume the data is Gaussian distributed, but turn out to be not usually assured in practice. A couple of graph-based subspaces learning algorithms has been proposed to reveal the local manifold structure of the face data hidden in the image space 4. The instances of graph-based algorithms include Locality Preserving Projection LPP 5 , Locally Linear Discriminate Embedding 6 and Neighbourhood Preserving Embedding NPE 7 These algorithms were shown to unfold the nonlinear structure of the face manifold by means of mapping nearby points in the high-dimensional space to the nearby points in a low-dimensional feature space.
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