基于空间频率能量分布的掌纹识别

提出了一种基于实数形式离散Gabor变换的掌纹特征提取方法,将空域的掌纹图像变换到联合空间频率域并将联合空间频率域的能量分布作为掌纹的特征,以此为基础使用欧式距离对特征向量进行了匹配识别。实验结果表明,该算法对掌纹图像小范围的平移、小角度的旋转和小范围的手掌伸缩具有鲁棒性,获得了比较理想的识别率。     

基于改进PCA和支持向量机的掌纹识别

掌纹识别是一种新兴的生物特征识别技术。掌纹识别是用掌纹特征(包括人眼可见的和不可见的)来进行身份鉴别的一种方法。其中掌纹特征提取和掌纹特征匹配是掌纹识别研究的关键部分和核心内容。在特征提取方面,给出了两种改进的特征提取方法。先对掌纹图像进行傅里叶变换,再对变换后的图像进行主成分分析;针对掌纹图像的特点,对PCA进行改进,设计了适用于掌纹图像的分块主成分算法。将一整幅掌纹图像分为若干子块图像,在此基础上进行主成分分析。通过实验验证了改进的特征提取方法可以提高识别准确率。在特征识别方面,模版匹配虽然在一定程度上计算量小,准确率高,但容易陷入小样本问题。因此通过训练SVM分类器,进行掌纹识别。实验证明该方法有较好的可行性。     

掌纹掌脉图像超小波域融合识别算法

针对单一生物特征识别技术易受外界各种因素影响,识别率和稳定性有待提高的问题,提出一种掌纹掌脉图像超小波域融合识别算法NSCT-NBP。首先,对掌纹掌脉图像利用非下采样Contourlet变换（NSCT）进行分解,将得到的低频和高频子图像分别利用区域能量和图像自相似原理进行融合;然后,对融合后的图像利用近邻二值模式（NBP）提取纹理特征,获得特征向量;最后,通过计算特征向量间的汉明距离比较融合图像间的近似程度来计算等误率（EER）。在PloyU图库及自建图库上进行实验,结果表明,NSCT-NBP算法可获得最低的EER,分别为0.72%和0.96%,识别时间仅为0.0530 s和0.0871 s,与当前最优的基于小波变换和Gabor滤波器的掌纹掌脉融合方法相比,在两个图库上EER分别降低了4%和36.8%。NSCT-NBP算法能够有效融合掌纹掌脉图像的纹理特征,具有良好的识别性能,并且掌纹掌脉特征的融合增强了识别系统的安全性。     

双树复小波变换结合LM神经网络的掌纹识别方案

针对现有掌纹识别方案不能够很好的提取多分辨率特征的问题,提出一种基于双树复小波变换（DT-CWT）和Levenberg-Marquardt（LM）神经网络的掌纹识别方案. 首先,将彩色手掌图像转换成灰度图像. 然后,提取出手掌图像中的感兴趣区域（ROI）,并构建成直方图. 接着,利用DT-CWT进行6层小波分解并获得特征系数,分别计算特征系数的最大值、平均值和中值构建36维特征向量. 最后,利用LM神经网络根据特征向量实现掌纹的识别分类. 在CASIA数据库上的实验结果表明,相比其他几种较新的识别方案,提出的方案的具有更高的识别率和更少的识别时间.     

改进Contourlet变换的3D掌纹图像识别

提出一种基于改进Contourlet变换的3D掌纹图像识别方法;该方法通过形状指数将3D掌纹图像映射成灰度图像,以克服常用的均值或高斯曲率映射难于精确描述3D掌纹特征的缺点;基于此,将7/5滤波器引入Contourlet变换,并在变换域提取形状指数映射图各方向子带的均值与方差作为掌纹图像的特征信息,从而有效利用了Contourlet变换优越的方向特征表达能力,又可有效消除传统Contourlet变换各子图像存在的相关性;最后采用欧氏距离最近邻分类法,实现了测试图像的分类识别。实验结果表明,针对香港理工大学所提供的三维掌纹数据库,该方法总体识别率较PCA方法提高了2.9%,具有明显的优势。     

小波变换与分块统计在掌纹识别中的应用

用于身份鉴别的掌纹识别为信息安全提供了一种新的方案。提出一种变换域和统计域相结合的掌纹识别方法。对掌纹感兴趣区域（ROI）进行中值滤波再多级小波分解,对所有的高频子图像进行分块,求取每一子块高频系数的均值和方差,它们的组合构成该图像的特征向量,利用简单的最近邻分类器进行分类。运用UST掌纹图像库,对该算法进行了测试。从识别率为95.5%的实验结果看,该方法优于目前在掌纹识别上使用较多的子空间法。     

基于低频能量分布的掌纹特征提取

提出了一种基于傅里叶变换的掌纹特征提取方法,利用傅里叶变换将空域的掌纹图像变换到频域,选取频域图像的低频能量分布作为掌纹的特征,以此为基础对特征向量进行匹配识别。实验结果表明,该算法实现简单、操作方便,具有较快的反应速度和较高的识别率。     

基于离散余弦变换和支持向量机的掌纹识别

提出一种利用离散余弦变换提取掌纹的特征,通过支持向量机进行掌纹分类识别的方法。在利用离散余弦变换进行特征提取时,将变换系数矩阵左上角的部分元素作为掌纹图像的特征;在利用支持向量机进行掌纹分类识别时,采用“一对多”的分类方案。实验结果表明该方法的有效性。     

基于平稳小波变换的掌纹特征提取与识别

掌纹识别作为一种重要的生物特征识别方法,其中的一个重要环节就是掌纹特征的提取。论文基于图像的多尺度分析的思想,提出了一种利用平稳小波的局部极值点来提取掌纹特征的方法。文中利用平稳小波变换,对图像进行不同方向的滤波,然后提取各方向的极值点并融合作为特征点。并以此为基础进行不同掌纹的匹配识别。     

应用小波变换和支持向量机的掌纹识别

提出了一种新颖的掌纹识别方案。应用两维的两通道和三通道小波变换来得到低频子带图像,然后将其系数作为特征进行提取。再选择支持向量机（Support Vector Machine,SVM）作为分类器。实验结果证明了这是一个简单而有效的识别方案,正确识别率可达100％。     

基于傅立叶变换的掌纹识别方法

掌纹识别是指由计算机自动识别哪些掌纹图像来自同一只手掌,哪些来自不同的手掌.在掌纹识别中,特征提取算法的优劣至关重要.提出了一种基于傅立叶变换的掌纹特征提取方法.该方法的基本思想是先将掌纹图像应用傅立叶变换转换到频域,然后在频域中进行特征提取和描述.提取出来的特征备用来索引掌纹数据库,以便当一个新的掌纹图像被输入时,可以很快确定该手掌是否已经在掌纹库中注册.该方法可以用来做基于人体生物特征的身份识别,在安全领域有广泛的应用前景.实验验证了该方法的有效性.     

融合全局和局部方向特征的掌纹识别方法

摘 要：掌纹识别是受到较多关注的生物特征识别技术之一。在各类掌纹识别的方法中, 基于方向特征的方法取得了很好的效果。为了进一步提升识别精度,提出一种融合全局和局部 方向特征的掌纹识别算法,主要融合了基于方向编码的方法、基于方向特征局部描述子的方法 和结合方向特征和相关滤波器的方法。其中前 2 种方法属于空间域方法,可很好地提取掌纹的 局部方向特征;而第 3 种方法属于频域方法,能有效地提取全局方向特征。在匹配值层对该 3 种方法的识别结果进行融合。本文算法在 2 个掌纹数据库上进行了验证,实验结果表明,本文 方法的识别性能明显优于其他几种掌纹识别方法。     

Wavelet Energy Feature Extraction and Matching for Palmprint Recognition

According to the fact that the basic features of a palmprint, including principal lines, wrinkles and ridges, have different resolutions, in this paper we analyze palmprints using a multi-resolution method and define a novel palmprint feature, which called wavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in different directions at different resolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each level to compute the weighted city block distance for recognition. The experimental results show that the order of the discriminabilities of each level WEF, from strong to weak, is the 4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation and translation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprint verification and palmprint identification, respectively. These results demonstrate the power of the proposed approach.     

Double-cohesion learning based multiview and discriminant palmprint recognition

Palmprint recognition has been widely used in security authentication. However, most of the existing palmprint representation methods are focused on a special application scenario using the hand-crafted features from a single-view. If the features become weak as the application scenario changes, the recognition performance will be degraded. To address this problem, we propose to comprehensively exploit palmprint features from multiple views to improve the recognition performance in generic scenarios. In this paper, a novel double-cohesion learning based multiview and discriminant palmprint recognition (DC_MDPR) method is proposed, which imposes a double-cohesion strategy to reduce the inter-view margins for each subject and the intra-class margins for each view. In this way, for each subject, the features from different views can be closer to each other in the binary-label space. Meanwhile, for each view, the features sharing the same label information can move towards each other by imposing a neighbor graph regularization. The proposed method can be flexibly applied to any type of palmprint feature fusion. Moreover, it presents the multiview features in a low-dimensionality sub-space, effectively reducing the computational complexity. Experimental results on various palmprint databases have shown that the proposed method can always achieve the best recognition performance compared to other state-of-the-art algorithms.     

Orientation selection using modified FCM for competitive code-based palmprint recognition

Coding-based methods are among the most promising palmprint recognition methods because of their small feature size, fast matching speed and high verification accuracy. The competitive coding scheme, one representative coding-based method, first convolves the palmprint image with a bank of Gabor filters with different orientations and then encodes the dominant orientation into its bitwise representation. Despite the effectiveness of competitive coding, few investigations have been given to study the influence of the number of Gabor filters and the orientation of each Gabor filter. In this paper, based on the statistical orientation distribution and the orientation separation characteristics, we propose a modified fuzzy C-means cluster algorithm to determine the orientation of each Gabor filter. Since the statistical orientation distribution is based on a set of real palmprint images, the proposed method is more suitable for palmprint recognition. Experimental results indicate that the proposed method achieves higher verification accuracy while compared with that of the original competitive coding scheme and several state-of-the-art methods, such as ordinal measure and RLOC. Considering both the computational complexity and the verification accuracy, competitive code with six orientations would be the optimal choice for palmprint recognition.     

局部二进制模式与小波特征融合的掌纹识别

针对利用单一方法进行掌纹图像识别所得的识别率难以提高这一情况,提出一种利用掌纹图像经高斯高通滤波后的局部二进制模式特征和三级小波分解的细节图像的能量特征的融合特征进行掌纹识别的方法。在提取图像的局部二进制模式特征的时候,通过高斯高通滤波增强图像的对比度,从而提取出更有效的局部二进制模式特征,该特征对光照的变化具有一定的鲁棒性;小波变换的细节图像能量数据反映不同频率成分的局部细节特征。实验结果表明所提出的掌纹识别方法的有效性。     

小波分解与PCA方法的掌纹特征提取方法*

提出了一种新的掌纹特征提取方法,其目的在于在不降低识别率的情况下,提高掌纹特征提取速度。首先将原始掌纹图像进行小波分解,获得低分辨率的掌纹图像;其次通过主成分分析（PCA）方法获得一个低维子空间,即“特征掌”;最后通过将训练、测试样本在该“特征掌”上投影来提取掌纹特征。实验结果表明,所提出方法与单一PCA方法比较,在同样识别率情况下,特征提取速度明显提高。     

On accurate orientation extraction and appropriate distance measure for low-resolution palmprint recognition

Orientation feature has been demonstrated to be one of the most effective features for low resolution palmprint recognition. In this paper, using steerable filter, we investigate the accurate orientation extraction and appropriate distance measure problems for effective palmprint recognition. First, we use high order steerable filter to extract accurate continuous orientation, and quantify it into discrete representation. Then, for effective matching of accurate orientations, we propose a generalized orientation distance measure. We further extend the distance measure for matching of discrete orientations, and show that several existing distance measures can be viewed as its special cases. Experimental results on both Hong Kong PolyU and CASIA palmprint databases show that the proposed method can obtain state-of-the-art verification accuracy. With the support of a look up table, the proposed method also enables small template size and satisfactory matching speed for practical applications.