Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96753
Title: Face recognition via local preserving average neighborhood margin maximization and extreme learning machine
Authors: Chen, Xiaoming
Liu, Wanquan
Lai, Jianhuang
Li, Zhen
Lu, Chong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Chen, X., Liu, W., Lai, J., Li, Z., & Lu, C. (2012). Face recognition via local preserving average neighborhood margin maximization and extreme learning machine. Soft Computing, 16(9), 1515-1523.
Series/Report no.: Soft computing
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.
URI: https://hdl.handle.net/10356/96753
http://hdl.handle.net/10220/12025
ISSN: 1432-7643
DOI: 10.1007/s00500-012-0818-4
Rights: © 2012 Springer-Verlag.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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