目的 将韦伯局部描述子WLD(Weber local descriptor)应用于掌纹识别,并针对掌纹具有丰富线特征的特点,在WLD基础上改进获得线特征韦伯局部描述子LWLD(line feature weber local descriptor),以提高掌纹识别的效率。方法 首先采用MFRAT或Gabor滤波器对掌纹图像进行线性滤波,生成方向图和能量图;然后对能量进行韦伯差分激励滤波生成差分激励图;最后,基于方向图和差分激励图构造线韦伯特征直方图,并基于线韦伯特征直方图进行掌纹特征识别。结果 基于PolyU Ⅱ和Cross-Sensor掌纹库进行对比实验,采用曼哈顿距离和卡方距离进行匹配,其中在PolyaⅡ库上的识别率最高均达到100%,在识别率和容错性方面均优于其他主要基于局部描述子的识别方法。结论 首次将韦伯局部描述子引入掌纹识别领域,发展了一种新的基于局部描述子的掌纹识别方法。和其他基于局部描述子的掌纹识别算法相比,本文方法具有更高识别率和稳定性。

Objective Given the advantages of low computation cost and absence of a training requirement, local descriptor-based palmprint recognition methods are eliciting an increasing amount of attention. The Weber local descriptor (WLD) is a newly presented local descriptor inspired by Weber's law in psychology. This study applies WLD to palmprint recognition. To improve palmprint recognition performance, a line feature WLD is presented by considering the sufficient line features of a palmprint. Method First,modified finite random transform or the Gabor filter is applied to a palmprint image to generate directional image Φ and energy image E. Second, energy image E is convoluted by the Weber operator to generate differential excitation image . Finally, based on directional image Φ and differential excitation image ξ, the histogram of the line Weber local feature can be constructed for use in palmprint recognition. Result The Polytechnic University Palmprint Database II and the Cross-Sensor Palmprint Database are utilized in an experiment on Polyu Ⅱ database. The proposed method can achieve 100% identification rate with both Manhattan and chi-square distance. Results demonstrate that the presented method has a high identification rate and is robust. Conclusion This study introduces WLD into palmprint recognition to develop a new palmprint recognition method based on a local descriptor. Compared with other palmprint recognition methods based on a local descriptor, the presented method has a higher identification rate and is more robust.