High-dimensional data often lie on relatively low-dimensional manifold, while the nonlinear geometry of that
manifold is often embedded in the similarities between the data points. These similar structures are captured by
Neighborhood Preserving Embedding (NPE) effectively. But NPE as an unsupervised method can’t utilize class
information to guide the procedure of nonlinear dimensionality reduction. They ignore the geometrical structure
information of local data points and the spatial information of pixels, which leads to the failure of classification. For this
problem, a feature extraction method based on Image Euclidean Distance-Supervised NPE (IED-SNPE) is proposed, and
is applied to facial expression recognition. Firstly, it employs Image Euclidean Distance (IED) to characterize the
dissimilarity of data points. And then the neighborhood graph of the input data is constructed according to a certain kind
of dissimilarity between data points. Finally, it fuses prior nonlinear facial expression manifold of facial expression
images and class-label information to extract discriminative features for expression recognition. In the classification
experiments on JAFFE facial expression database, IED-SNPE is used for feature extraction and compared with NPE,
SNPE, and IED-NPE. The results reveal that IED-SNPE not only the local structure of expression manifold preserves
well but also explicitly considers the spatial relationships among pixels in the images. So it excels NPE in feature
extraction and is highly competitive with those well-known feature extraction methods.
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