基于多尺度Gabor方向韦伯局部描述子的掌纹识别

Palmprint Recognition Based on Multi-Scale Gabor Orientation Weber Local Descriptors

韦伯局部描述子(WLD)是一种有效的图像特征描述子。但是,构成WLD特征的差分激励和梯度方向无法准确地刻画掌纹图像的局部灰度变化和纹线的方向,因此基于WLD的掌纹识别性能并不高。针对掌纹图像纹线特征较丰富的特点,对WLD特征进行改进获得多尺度Gabor方向韦伯局部描述子,以提高掌纹识别的性能。首先,采用多尺度Gabor滤波器对掌纹图像进行滤波,得到多尺度能量图和方向图;然后,基于能量图计算差分激励;最后,基于多尺度差分激励图和方向图构造直方图特征,并将不同尺度下的特征向量串联,进而生成掌纹图像的最终特征集。在PolyU,PolyU Multi-spectral和CASIA三种数据库上的实验结果表明,本文方法与一些现有的掌纹识别方法相比,具有较高的识别率和较低的等错误率。

Weber local descriptor(WLD)is an effective image feature descriptor.However,the differential excitation and gradient orientation,which are two components of WLD,can not accurately describe the difference of local image blocks and the orientation of palm lines,so the performance of palmprint recognition based on WLD features is not high.In order to improve palmprint recognition performance,multi-scale Gabor orientation Weber local descriptors are proposed in view of the rich line features of palmprint images.First,multi-scale Gabor filter is used to filter the palmprint image to generate multi-scale energy maps and orientation maps.Then,the differential excitation is calculated based on energy maps.Finally,the histogram features are constructed based on multi-scale differential excitation maps and orientation maps,and the feature vectors from different scales are then concatenated to produce the final feature set of a palmprint image.The experiments on PolyU,PolyU Multi-spectral and CASIA palmprint databases show that the proposed method can achieve higher identification rate and lower equal error rate compared with some existing palmprint recognition methods.