掌纹识别是生物识别领域的一个重要分支。
Palmprint recognition is a very important topic in the biometric technology.
首次提出将局部二进制模式(LBP)应用到掌纹识别中。
This paper first presents a new method of palmprint identification by using Local Binary Pattern (LBP).
该文提出一种新的用于掌纹识别的纹线结构特征描述和匹配方法。
This paper presents a new approach to line structure features representation and matching for palmprint recognition.
掌纹识别技术是根据人手掌内的有效信息来对个人身份进行识别。
Palmprint identification can identify individual's status based on the valid information in his or her palm.
此领域的专家旨在研究数码指纹、面部识别、视网膜识别、掌纹识别等技术。
Biometrics specialists are developing technology for digital fingerprinting, facial recognition, retina identification, hand geometry recognition, and so on.
提出了一种具有抗噪性能的二值图像边缘跟踪算法，并将其应用在掌纹识别中。
An edge tracing algorithm based on binary image and its application on palm recognition is introduced.
作为最重要的生物特征识别技术之一，掌纹识别方法的开发具有重要的现实意义。
As one of the most important biometrics techniques, the development of palmprint recognition has a significant influence on real world.
为了保持掌纹空间的局部结构，运用局部保持投影(LPP)方法进行掌纹识别。
In order to preserve the local structure of the image space, locality preserving projection (LPP) is applied to palmprint recognition.
作为最重要的生物特征识别技术之一，掌纹识别方法的开发具有重要的现实意义。
As the one of the most important biometric recognition, the development of Palmprint recognition has meaningful value of practice.
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.
一种掌纹识别方法，包括：分析来自手掌图像的区域，以利用该区域获得皮肤表面的纹理数据。
A method of palmprint identification includes analyzing an area from an image of a palm to obtain texture data for the skin surface with the area.
其中掌纹识别是生物特征识别技术中一种新颖的身份识别方法，并在理论及应用研究受到了多方的重视。
Furthermore, the palmprint identification is a new method of the biometric identification technology, and its theoretical and applied research has been paid attention from multi-branch.
掌纹识别技术利用人的掌部纹理作为生物特征进行身份的自动确认，是生物特征识别领域的又一新兴技术。
The palmprint recognition is a new biometrics technology, which use palm texture as biologic characters to recognize identity.
掌纹自动识别是对基于生物统计学的身份鉴别的重要补充。
Automated palmprint recognition is an important part of biometric based personal identification.
作为一种较新的生物特征，掌纹可用来进行人的身份识别。
A palmprint is a relative new biometric feature for personal authentication.
截至目前，已经用于身份识别的人体生物特征包括指纹、掌纹、虹膜、人脸、手形、签名等。
Up to now, many traits in human body have been applied, such as fingerprint, palmprint, iris, face, hand shape and signature, etc.
其中生物识别门禁又包括指纹、虹膜、掌纹、语音等系统。
And the biometric identification entrance guard system includes fingerprint system, iris system, palm print system and voice system and so on.
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。
The feature extraction of palmprint is a necessary important step in automatic palmprint recognition system.
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.
提出了一种新的基于视觉特征的掌纹图像识别方法。
A new palmprint recognition method based on visual characteristics is proposed.
在此基础上结合子空间法模式识别理论 ，提出了双子空间掌纹自动识别方法。
Therefore, the dual eigenspace method for automatic palmprint recognition was proposed using the subspace method of pattern recognition.
因为人的手掌纹理相对于很多其它的人体生物学特征更具有稳定性、清晰性和易于采集性，因而出现了许多针对人体掌纹的识别算法。
The human palmprint is more stable, more clearly represented and easier to collect compared with other human biological characteristics, thus many palmprint recognition algorithms are developed.
生物特征识别目前主要研究现场指纹、交叉库指纹的匹配问题以及无接触掌纹的识别问题。
Biometric recognition research in our group mainly includes latent fingerprint matching, cross matching and contactless palmprint matching.
生物特征识别目前主要研究现场指纹、交叉库指纹的匹配问题以及无接触掌纹的识别问题。
Biometric recognition research in our group mainly includes latent fingerprint matching, cross matching and contactless palmprint matching.
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