A Two-Pass Classification Method Based on Hyper-Ellipsoid Neural Networks and SVM's with Applications to Face Recognition

Authors:
Chengan Guo;Chongtao Yuan;Honglian Ma
Affiliations:
School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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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.