Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2007-06-03

Included Journals: CPCI-S、EI

Volume: 4493

Issue: PART 3

Page Number: 461-+

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.