Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Radial Basis Probabilistic Neural Networks Committee for Palmprint Recognition
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Personal Identification Using Palmprint and Contourlet Transform
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A survey of palmprint recognition
Pattern Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
An evaluation of wavelet kernels for palmprint based recognition
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
A feature level multimodal approach for palmprint identification using directional subband energies
Journal of Network and Computer Applications
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This paper proposes a novel method for recognizing palmprint using the winner-take-all (WTA) network based independent component analysis (ICA) algorithm and the radial basis probabilistic neural network (RBPNN) proposed by us. The WTA-ICA algorithm exploits the maximization of the sparse measure criterion as the cost function, and it extracts successfully palmprint features. The classification performance is implemented by the RBPNN. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS (ROLS) algorithm. Experimental results show that the RBPNN achieves higher recognition rate and better classification efficiency with other usual classifiers.