The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Face recognition with radial basis function (RBF) neural networks
Abstract
A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.
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Lu, J., Plataniotis, K. N., & Venetsanopoulos, A. N. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.806629 · Cited by 608
Self-citationRecent advances in visual and infrared face recognition—a review
Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.04.001 · Cited by 586
Independent component analysis of gabor features for face recognition
Liu, C., & Wechsler, H. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.813829 · Cited by 466
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Shen, L., & Bai, L. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0033-y · Cited by 462
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Metadata
- Authors
- Er, M. J., Wu, S., Lu, J., & Toh, H. L.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2002-05
- Volume
- 13
- Issue
- 3
- Pages
- 697-710
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2158247472
- Cited by
- 664
- References
- 42
- FWCI
- 16.85
- Type
- journal article
- Language
- en
- Open access
- closed
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