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Soft Computing

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01.09.2012 | Focus

Face recognition via local preserving average neighborhood margin maximization and extreme learning machine

verfasst von: Xiaoming Chen, Wanquan Liu, Jianhuang Lai, Zhen Li, Chong Lu

Erschienen in: Soft Computing | Ausgabe 9/2012

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Abstract

Average neighborhood maximum margin (ANMM) is an effective method for feature extraction in appearance-based face recognition. In this paper, we extend ANMM to locality preserving average neighborhood margin maximization (LPANMM) in order to maintain the local structure on the original data manifold in the discriminant feature space. We also combine LPANMM with extreme learning machine (ELM) as a new scheme for face recognition, we train the single-hidden layer feedforward neural network (SLFN) in the ELM classifier with the discriminant features that are extracted by LPANMM, then we use the trained ELM classifer to classify the test data. In the process of training SLFN, ELM can not only achieve the smallest training error in theory, but is also not sensitive to the initial value selection of the parameters for the SLFN. Experimental results on ORL, Yale, CMU PIE and FERET face databases demonstrate the scheme LPANMM/ELM can achieve better performance than ANMM and other traditional schemes for face recognition.

Vorheriger Artikel Generalized extreme learning machine acting on a metric space

Nächster Artikel Face recognition with lattice independent component analysis and extreme learning machines

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Titel: Face recognition via local preserving average neighborhood margin maximization and extreme learning machine
verfasst von: Xiaoming Chen
Wanquan Liu
Jianhuang Lai
Zhen Li
Chong Lu
Publikationsdatum: 01.09.2012
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 9/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0818-4

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