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- 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
- 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.
- Subjects
HUMAN facial recognition software; FACE perception; MACHINE learning; FEATURE extraction; PATTERN recognition systems
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2012, Vol 16, Issue 9, p1515
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-012-0818-4