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- Title
Feature extraction based on graph discriminant embedding and its applications to face recognition.
- Authors
Huang, Pu; Li, Tao; Gao, Guangwei; Yang, Geng
- 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.
- Subjects
HUMAN facial recognition software; FEATURE extraction; GRAPH algorithms; EMBEDDINGS (Mathematics); DATA structures
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2019, Vol 23, Issue 15, p7015
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-018-3340-5