Palmprint identification based on non-separable wavelet filter banks

Creases, as a special salient feature of palmprint, are large in number and distributed at all directions. It changes slowly in a person's whole life, which qualifies themselves as features in palmprint identification. In this paper, we devised a new algorithm of crease extraction by using non-separable bivariate wavelet filter banks with linear phase. Compared with the traditional wavelet, our research demonstrates that the three high frequency sub-images generated by Non-separable Discrete Wavelet Transform (NDWT) can extract more creases and no longer extensively focus on the three special directions. As a consequence, we proposed a new method by combining NDWT and Support Vector Machines (SVM) for palmprint identification. Tested by our experiment, this method achieves a satisfied identification result and computational efficiency as well.