融合水平梯度与局部信息强度的掌纹识别算法

掌纹纹线特征是掌纹最有效的特征.由于在采集掌纹时不可避免地会产生尺度不一致、细微的旋转或平移等问题,使得准确地提取以及描述纹线特征成为掌纹识别的一大难点.针对这一问题,提出了一种融合水平梯度与局部信息强度的掌纹识别算法(Horizontal Gradient-Local Information Intensity,HG-LII).首先,使用不同的均值滤波模板消除细小、不规则、不稳定的掌纹纹线特征,对处理后的图像使用水平梯度算子得到水平方向的梯度图像,并进行二值化;其次使用分块思想计算掌纹纹线的信息强度,并将其作为特征向量;最后采用卡方距离进行匹配,判断掌纹所属类别.在PolyU掌纹库上的实验结果表明,该算法识别率达到99.89％,与传统的提取纹线算法相比,识别率有明显的提高,表明了该算法的有效性.

Palmprint Recognition Algorithm of Integrating Horizontal Gradient and Local Information Intensity

The palm ridge characteristic is the most effective feature of palmprint.At the acquisition time,palm will produce problems of inconsistent scale,rotation,translation and so on,which make accurately extracting and describing ridge feature become a major difficulty in palmprint recognition.To solve these problems,a novel palmprint recognition algorithm based on a fusion of horizontal gradient orientation and local information intensity(referred FHOG-LII) was proposed.First,we used the mean filter with different templates to remove fine,irregular and unstable characteristics of palm ridge,and used horizontal gradient operator to handle the processed image the processed image and then made image binarization.Secondly,we used the thoughts of blocks to calculate information intensity of palm ridge and made it as feature vector.At last,we used the chi-square distance to match them.Experimental results on PolyU palmprint database show that the proposed method can obtain state-of-the-art recognition accuracy(99.89%).Compared with some traditional methods,the recognition rate improves significantly,indicating the effectiveness of the proposed algorithm.