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Soft Computing

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22.06.2018 | Methodologies and Application

Feature extraction based on graph discriminant embedding and its applications to face recognition

verfasst von: Pu Huang, Tao Li, Guangwei Gao, Geng Yang

Erschienen in: Soft Computing | Ausgabe 16/2019

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Abstract

Graph embedding-based learning methods have been widely employed to reduce the dimensionality of high-dimensional data, while how to construct adjacency graphs to discover the essential structure of the data is the key problem in these methods. In this paper, we present a novel algorithm called graph discriminant embedding (GDE) for feature extraction and recognition. GDE combines local information and label information of data points to construct two neighbor graphs, which help to pull the same-class samples nearer and nearer and repel the not-same-class samples farther and farther when they are projected onto a feature subspace. Significantly differing from most of the other graph embedding methods, GDE does not only emphasize the importance of the nearby points but also enhance the importance of the distant points which may have potential advantages for classification. Experimental results on the AR, CMU PIE and FERET face databases demonstrate the effectiveness of the proposed algorithm.

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Titel: Feature extraction based on graph discriminant embedding and its applications to face recognition
verfasst von: Pu Huang
Tao Li
Guangwei Gao
Geng Yang
Publikationsdatum: 22.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3340-5

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