Radial Basis Probabilistic Neural Networks Committee for Palmprint Recognition

Authors:
Jixiang Du;Chuanmin Zhai;Yuanyuan Wan
Affiliations:
Department of Computer Science and Technology, Huaqiao University, China and Department of Automation, University of Science and Technology of China;Department of Computer Science and Technology, Huaqiao University, China;Department of Automation, University of Science and Technology of China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

In this paper, a novel and efficient method for recognizing palmprint based on radial basis probabilistic neural networks committee (RBPNNC) was proposed. The RBPNNC consists of several different independent neural networks trained by different feature domains of the original images. The final classification results represent a combined response of the individual networks. The Hong Kong Polytechnic University (PolyU) palmprint database is exploited to test our approach. The experimental results show that the RBPNNC achieves higher recognition accuracy and better classification efficiency than single feature domain.