These two techniques are effectively combined in the paper, and a new palmprint feature extraction approach is proposed.
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。
As the crucial part of palmprint recognition, this paper mainly focuses on the ideas and usefulness and implementation of these two feature extraction algorithms.
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。
The feature extraction of palmprint is a necessary important step in automatic palmprint recognition system.
掌纹特征的提取在掌纹自动识别系统中是一个必不可少的重要环节。
A palmprint is a relative new biometric feature for personal authentication.
作为一种较新的生物特征，掌纹可用来进行人的身份识别。
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。
Furthermore, on the basis of feature extraction, by utilizing the Radial basis Probabilistic Neural Networks (RBPNN), the palmprint recognition task could be implemented automatically.
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.
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。
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