低定位精度条件下掌纹图像识别

提出了一种多层次相位相关掌纹识别(HPCPR)算法.先采用改进的结合相关值位置和大小的方法在整体上匹配和对齐掌纹图像,再用分块的加权相位相关法(WPC)精确匹配对齐后的掌纹图像,实现了低定位精度条件下掌纹图像的有效识别.算法综合使用掌纹图像的整体和局部特征,且能更快地对齐待匹配图像.与传统基于相位相关的识别方法相比,该算法既提高了识别的精度,又在识别的效率上获得明显改进.在PolyU掌纹库上的测试结果验证了算法的良好效果.

Palmprint recognition using images with low location accuracy

A hierarchical phase correlation palmprint recognition (HPCPR) algorithm is presented for the low location accuracy palmprint image recognition. The global image matching and alignment are accomplished using the peak value and peak position of phase correlation function, and then a fine matching is realized through computing the weighted phase correlation of all sub-blocks taken from the coincident area of the two images. As HPCPR algorithm employs both global and local features of palmprint image and can quickly align candidate matching images, it outperforms the conventional phase correlation method in both recognition rate and efficiency. Experiments on PolyU Palmprint Database verified the effectiveness of this approach.