Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Springer-Verlag
2012
|
| Online Access: | http://hdl.handle.net/20.500.11937/6000 |
| _version_ | 1848744952573460480 |
|---|---|
| author | Chen, Xiaoming Liu, Wan-Quan Lai, J. Li, Z. Lu, C. |
| author_facet | Chen, Xiaoming Liu, Wan-Quan Lai, J. Li, Z. Lu, C. |
| author_sort | Chen, Xiaoming |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:09:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-6000 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:09:38Z |
| publishDate | 2012 |
| publisher | Springer-Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-60002017-09-13T14:41:37Z Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine Chen, Xiaoming Liu, Wan-Quan Lai, J. Li, Z. Lu, C. 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. 2012 Journal Article http://hdl.handle.net/20.500.11937/6000 10.1007/s00500-012-0818-4 Springer-Verlag restricted |
| spellingShingle | Chen, Xiaoming Liu, Wan-Quan Lai, J. Li, Z. Lu, C. Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title | Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title_full | Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title_fullStr | Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title_full_unstemmed | Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title_short | Face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| title_sort | face recognition via local preserving average neighbourhood margin maximization and extreme learning machine |
| url | http://hdl.handle.net/20.500.11937/6000 |