Characterization of palmprints by wavelet signatures via directional context modeling

The palmprint is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Current palmprint-based systems are more user friendly, more cost effective, and require fewer data signatures than traditional fingerprint-based identification systems. The principal lines and wrinkles captured in a low-resolution palmprint image provide more than enough information to uniquely identify an individual. This paper presents a palmprint identification scheme that characterizes a palmprint using a set of statistical signatures. The palmprint is first transformed into the wavelet domain, and the directional context of each wavelet subband is defined and computed in order to collect the predominant coefficients of its principal lines and wrinkles. A set of statistical signatures, which includes gravity center, density, spatial dispersivity and energy, is then defined to characterize the palmprint with the selected directional context values. A classification and identification scheme based on these signatures is subsequently developed. This scheme exploits the features of principal lines and prominent wrinkles sufficiently and achieves satisfactory results. Compared with the line-segments-matching or interesting-points-matching based palmprint verification schemes, the proposed scheme uses a much smaller amount of data signatures. It also provides a convenient classification strategy and more accurate identification.     

Palmprint verification based on principal lines

In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong.     

一种用于掌纹识别的线特征表示和匹配方法

作为一种较新的生物特征,掌纹可用来进行人的身份识别.在用于身份识别的诸多特征中,掌纹线,包括主线和皱褶,是最重要的特征之一.本文为掌纹识别提出一种有效的掌纹线特征的表示和匹配方法,该方法定义了一个矢量来表示一个掌纹上的线特征,该矢量称为线特征矢量(1ine feature vector,简称LFV).线特征矢量是用掌纹线上各点的梯度大小和方向来构造的.该矢量不但含有掌纹线的结构信息,而且还含有这些线的强度信息,因而,线特征矢量不但能区分具有不同线结构的掌纹,同时也能区分那些具有相似的线结构但各线强度分布不同的掌纹.在掌纹匹配阶段,用互相关系数来衡量不同线特征矢量的相似性.实验表明,LFV方法无论是在速度、精度,还是在存储量方面都能满足联机生物识别的要求.     

Palmprint verification using hierarchical decomposition

A reliable and robust personal verification approach using palmprint features is presented in this paper. The characteristics of the proposed approach are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, a flatbed scanner is adopted as an input device for capturing palmprint images; it has the advantages of working without palm inking or a docking device. In the proposed approach, two finger-webs are automatically selected as the datum points to define the region of interest (ROI) in the palmprint images. The hierarchical decomposition mechanism is applied to extract principal palmprint features inside the ROI, which includes directional and multi-resolution decompositions. The former extracts principal palmprint features from each ROI. The latter process the images with principal palmprint feature and extract the dominant points from the images at different resolutions. A total of 4800 palmprint images were collected from 160 persons to verify the validity of the proposed palmprint verification approach and the results are satisfactory with acceptable accuracy (FRR: 0.75% and FAR: 0.69%). Experimental results demonstrate that our proposed approach is feasible and effective in palmprint verification.     

Development of Palmprint Verification System Using Biometrics

1 Introduction Reliability in personal authentication is key to the security in any transactional database. Many physiological characteristics of humans i.e., biometrics, are typically time invariant, easy to acquire, and unique for every individual. Biom…     

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。     

基于二阶段小波多分辨率分析的掌纹分割算法

针对复杂掌纹纹线难以分割、有效性低的问题,提出一种基于二阶段小波多分辨率分析的掌纹分割算法.该方法首先利用小波多分辨率分析高频子图的候选子区域,对得到的相似掌纹纹线集合进行合并;接着对合并相似区域的集合和二值化集合求交集得到融合图像;最后利用区域生长法和形态学去噪得到掌纹主要纹理特征.实验结果表明,该方法不仅能有效地剔除复杂掌纹的噪声,而且能准确提取掌纹特征,从而达到准确识别的目的.     

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。     

基于八元数的彩色掌纹特征提取与识别算法

综合4种传统算法和八元数BP神经网络,提出一种掌纹特征提取算法,自动提取彩色掌纹图像的掌纹线。对掌纹线图像进行二维小波分解,并构造七维特征向量,采用八元数矢量积表示算法进行掌纹识别。实验结果表明,掌纹提取算法能提取出较精细的掌纹线,识别算法的成功率可达96%。     

基于Gabor小波变换和最佳鉴别特征的掌纹识别

提出了一种提取掌纹图像特征的方法,该方法的实现过程如下：首先,计算掌纹图像上均布离散位置的二维Gabor小波变换系数的幅值,将其作为掌纹图像的原始特征;其次,利用主分量分析实现Gabor小波特征的降维;最后,通过线性判别分析提取最有利于分类的最佳鉴别特征。实验结果表明了该方法的有效性。     

基于掌纹、人脸在特征层融合的身份识别算法

首先利用小波变换增强掌纹、人脸图像;然后利用一种新的子空间分析方法——对角离散余弦变换和二维主元判别分析(Diagonal,Discrete Cosine Transform and Two-Dimensional Principle Component Analysis,Dia-DCT+2DPCA)相结合的算法提出了一种掌纹、人脸特征融合的识别方法;最后运用最小距离分类器进行识别。实验结果表明,该文提出的掌纹、人脸特征融合方法实现了特征层融合,有效地提高了身份识别的正确识别率。     

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。     

Improved discriminant locality preserving projections for face and palmprint recognition

We propose in this paper two improved manifold learning methods called diagonal discriminant locality preserving projections (Dia-DLPP) and weighted two-dimensional discriminant locality preserving projections (W2D-DLPP) for face and palmprint recognition. Motivated by the fact that diagonal images outperform the original images for conventional two-dimensional (2D) subspace learning methods such as 2D principal component analysis (2DPCA) and 2D linear discriminant analysis (2DLDA), we first propose applying diagonal images to a recently proposed 2D discriminant locality preserving projections (2D-DLPP) algorithm, and formulate the Dia-DLPP method for feature extraction of face and palmprint images. Moreover, we show that transforming an image to a diagonal image is equivalent to assigning an appropriate weight to each pixel of the original image to emphasize its different importance for recognition, which provides the rationale and superiority of using diagonal images for 2D subspace learning. Inspired by this finding, we further propose a new discriminant weighted method to explicitly calculate the discriminative score of each pixel within a face and palmprint sample to duly emphasize its different importance, and incorporate it into 2D-DLPP to formulate the W2D-DLPP method to improve the recognition performance of 2D-DLPP and Dia-DLPP. Experimental results on the widely used FERET face and PolyU palmprint databases demonstrate the efficacy of the proposed methods.     

一种自适应的T型结构掌纹分区算法

掌纹图像可由一个T型结构分为指根区域、内侧区域和外侧区域3个部分,合理地利用这些分区信息,可以有效地提高掌纹识别的效率和正确率.为此,提出一种自适应的T型结构分区算法,利用掌纹中的主线信息,并结合掌纹的灰度和方向场构造一个目标函数;通过搜索寻找目标函数的最大值实现T型结构的定位,以实现对掌纹的分区.实验结果表明,采用文中的算法能够获得有效的掌纹分区结果.     

掌纹图像处理方法的研究

提出了一种利用图像方向信息提取掌纹特征纹线的方法。该方法将掌纹图像分成若干子块，充分利用子块中图像纹理的方向信息对掌纹图像进行滤波和增强处理；剔除图像中不含纹线的图像子块，对含有特征纹线的子块在其主方向上进行方向增强处理，突出特征纹线信息。对不同采集质量的掌纹图像的处理结果表明文中提出的方法是一种有效的掌纹图像处理方法，它可以应用于不同质量掌纹图像特征纹线的提取。     

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。     

采样式多通道Gabor滤波对掌纹的特征提取和分类

掌纹识别已被证实为最方便和有效的身份识别方法之一。根据掌纹的性质提出了一种掌纹方向特征提取的新方法,该方法首先利用选取掌纹中最拟合椭圆的方法寻找感兴趣区域,然后利用适应人感官系统的多通道采样式Gabor滤波器进行滤波,并提出用根据掌纹纹理和方向特性动态选取Gabor滤波器参数的方法来设计滤波器。在滤波过程中,从不同分辨率入手,利用不同方向和宽度的滤波器分别对掌纹的主线、褶皱、嵴线进行提取,在极坐标系下用改进的环行方向投影算法计算块能量,并且进行编码。经过模糊C均值聚类方法验证,结果表明,该方法对于掌纹具有很强的识别能力。     

一种新颖的低分辨率条件下的掌纹识别方法 *

掌纹识别是利用人的手掌图像进行身份鉴别的一种新兴生物特征识别技术。当今的掌纹识别方法存在的比较困难的问题之一就是在特征提取时受一些客观因素制约 ,影响后面的匹配精度。为此 ,提出一种改进的基于相位相关的掌纹图像匹配算法。该算法不受光照、平移的影响 ,对噪声也有很好的鲁棒性。在此基础上实验了该算法在不同分辨率下的表现 ,发现当分辨率降为原图像的十六分之一时 ,该算法仍然能取得较高的识别率 ,证明了该算法在较低分辨率下的有效性。     

基于àtrous-Contourlet变换与不变矩的掌纹匹配算法

为了提高掌纹识别的速度和准确率,克服Contourlet变换在处理高维信号时的不足,提出了一种新的掌纹识别算法。该算法首先对掌纹图像进行àtrous-Contourlet变换,得到高频分量和不同方向不同子带上的低频分量,再根据不同子带的能量分布所提取出的统计特征选择不同的特征加权系数,对图像所得到的不变矩向量进行加权计算,得到新的特征向量,完成掌纹图像的识别。实验结果表明,该算法与小波矩算法、Hu不变矩算法和Contourlet算法相比有较高的效率和匹配精度。     

基于小波分解的独立分量掌纹识别方法

独立分量分析方法在图像处理中具有独特的优势，用于掌纹特征提取，使得变换后的各分量之间不仅互不相关，而且还尽可能的统计独立，能更全面的揭示掌纹特征间的本质结构。为了降低运算复杂度，提出了一种基于小波分解的独立分量掌纹特征提取方法。首先对掌纹图像做小波变换进行降维，在保留原始图像轮廓信息和细节信息的基础上，去掉高频噪声，然后进行独立分量分析，采用FastICA算法，试验结果表明,本方法比传统的独立分量分析方法的识别率更高，且计算量大大减少。