本文将这两种技术有机地结合起来，提出了一种新的掌纹特征提取方法。
These two techniques are effectively combined in the paper, and a new palmprint feature extraction approach is proposed.
基于特征手的掌纹特征提取算法是根据类内类间距离准则而设计的特征提取算法。
Eigenpalms based feature extraction is designed according to the rule of in-class distance and between-class distance.
作为掌纹识别的关键部分，本文着重阐述两种特征提取算法的思想、作用和实现过程。
As the crucial part of palmprint recognition, this paper mainly focuses on the ideas and usefulness and implementation of these two feature extraction algorithms.
在特征提取的基础上，进一步利用径向基概率神经网络(RBPNN)分类器，实现了掌纹的自动识别。
Furthermore, on the basis of feature extraction, by utilizing the Radial basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically.
由于在线掌纹图象的对比度较低，掌纹中的细小皱纹和乳突纹会形成一定的噪声，掌纹中的线特征的模式十分复杂，因此对掌纹进行线特征提取是一项具有挑战性的研究课题。
Line patterns in palmprint are very complicated because of the low contrast and the heavy noise caused by fine wrinkles and ridges, so it is a challenging task to complete the feature extraction.
由于在线掌纹图象的对比度较低，掌纹中的细小皱纹和乳突纹会形成一定的噪声，掌纹中的线特征的模式十分复杂，因此对掌纹进行线特征提取是一项具有挑战性的研究课题。
Line patterns in palmprint are very complicated because of the low contrast and the heavy noise caused by fine wrinkles and ridges, so it is a challenging task to complete the feature extraction.
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