On-line fast palmprint identification based on adaptive lifting wavelet scheme

The recent on-line palmprint recognition algorithms are time-consuming, and not suitable for being implemented with hardware. This paper describes a novel on-line fast palmprint identification approach. In order to reduce the computational cost of extracting palmprint features from a palmprint image and make it easy to be implemented with hardware, we construct an adaptive lifting wavelet scheme to decompose a palmprint image into several subbands, and then the pulse-coupled neural network is employed to decompose each subband into a series of binary images. The entropies of these binary images are calculated and regarded as features. Then, in the classification step, a support vector machine-based classifier is utilized. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages compared with the recent on-line palmprint recognition algorithms. It is also shown that the proposed approach yields observably low computational cost and can be easily implemented with hardware.