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A Two-Pass Classification Method Based on Hyper-Ellipsoid Neural Networks and SVM's with Applications to Face Recognition.
- Source :
- Advances in Neural Networks: ISNN 2007 (9783540723943); 2007, p461-468, 8p
- Publication Year :
- 2007
-
Abstract
- In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN's) and the SVM's with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN's, and the second pass is followed by using the SVM's to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN's and the SVM's while remedying their disadvantages. Compared with the HENN's and the SVM's, a significant improvement of recognition performance over them has been achieved by the new method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540723943
- Database :
- Complementary Index
- Journal :
- Advances in Neural Networks: ISNN 2007 (9783540723943)
- Publication Type :
- Book
- Accession number :
- 33155033
- Full Text :
- https://doi.org/10.1007/978-3-540-72395-0_59