Robust palmprint verification using 2D and 3D features

This paper presents a new personal authentication system that simultaneously exploits 2D and 3D palmprint features. The objective of our work is to improve accuracy and robustness of existing palmprint authentication systems using 3D palmprint features. The proposed multilevel framework for personal authentication efficiently utilizes the robustness (against spoof attacks) of the 3D features and the high discriminating power of the 2D features. The developed system uses an active stereo technique, structured light, to simultaneously capture 3D image or range data and a registered intensity image of the palm. The surface curvature feature based method is investigated for 3D palmprint feature extraction while Gabor feature based competitive coding scheme is used for 2D representation. We comparatively analyze these representations for their individual performance and attempt to achieve performance improvement using the proposed multilevel matcher that utilizes fixed score level combination scheme to integrate information. Our experiments on a database of 108 subjects achieved significant improvement in performance with the integration of 3D features as compared to the case when 2D palmprint features alone are employed. We also present experimental results to demonstrate that the proposed biometric system is extremely difficult to circumvent, as compared to the currently proposed palmprint authentication approaches in the literature.     

Texture-based palmprint retrieval using a layered search scheme for personal identification

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered: feature extraction, similarity measurement and fast search for the best match of the queried image in an image database. We propose a texture-based approach for palmprint feature representation. The concept of texture energy is introduced to define both global and local features of a palmprint, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The searching is carried out in a layered fashion: the global features are first used to guide the fast selection of a small set of similar candidates from the database and then the local features are applied to determine the final output from the selected set of similar candidates. The experimental results illustrate the effectiveness of the proposed approach.     

Hierarchical palmprint identification via multiple feature extraction

Biometric computing offers an effective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.     

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.     

基于主线特征的双向匹配的掌纹识别新方法

掌纹识别是利用人的手掌掌纹图像对其身份进行认证的一种生物特征识别技术，目前的掌纹研究主要集中在掌纹特征线的提取算法上，而对特征线的筛选和匹配的问题讨论较少，掌纹上的纹线比较复杂，深浅粗细长短不一，实施任何一种边缘提取算法都要考虑纹线的取舍问题，首先介绍了提出的应用最大内切圆对掌纹有效区域进行分割和对准的方法，较好地解决了掌纹的定位问题，然后提出了掌纹特征线族的概念，用以刻画掌纹上的主要特征，从而将掌纹纹线特征分为主要特征和次要特征．通过对主要特征与全部特征的双向匹配，给出最终的识别结果，将该方法与之前提出的基于傅里叶变换的方法在自行研制的掌纹采样设备所采集的掌纹库(90人450幅)上进行了比较实验，实验结果证明新方法可以处理原方法无法定位的掌纹图像，同时识别率也有明显提高。     

Palmprint feature extraction using 2-D Gabor filters

Biometric identification is an emerging technology that can solve security problems in our networked society. A few years ago, a new branch of biometric technology, palmprint authentication, was proposed (Pattern Recognition 32(4) (1999) 691) whereby lines and points are extracted from palms for personal identification. In this paper, we consider the palmprint as a piece of texture and apply texture-based feature extraction techniques to palmprint authentication. A 2-D Gabor filter is used to obtain texture information and two palmprint images are compared in terms of their hamming distance. The experimental results illustrate the effectiveness of our method.     

一种新的掌纹图像感兴趣区域提取算法

重点研究具有一定自由度在线掌纹图像的感兴趣区域提取算法。首先结合掌纹图像的特点采用全局阈值二值化掌纹图像,然后利用形态学算子平滑掌纹轮廓,提取轮廓线Freeman链码并对链码进行角度变换,最后通过考察轮廓线上各点附近轮廓线的角度变化来提取掌纹图像感兴趣所需要的定位点,从而提取感兴趣区域。感兴趣区域的提取为特征提取和特征匹配打下了基础。最后,在两个公开的掌纹数据库,通过实验证明了这种算法的有效性。     

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。     

Two novel characteristics in palmprint verification: datum point invariance and line feature matching

As the first attempt of automatic personal identification by palmprint, in this paper, two novel characteristics, datum point invariance and line feature matching, are presented in palmprint verification. The datum points of palmprint, which have the remarkable advantage of invariable location, are defined and their determination using the directional projection algorithm is developed. Then, line feature extraction and line matching are proposed to detect whether a couple of palmprints are from the same palm. Various palmprint images have been tested to illustrate the effectiveness of the palmprint verification with both characteristics.     

基于LEM的在线掌纹识别

主要研究利用掌纹对人进行身份鉴定。在掌纹的各种特征中，线特征是一种非常重要的特征，但由于掌纹线不规则，几乎不能用数学进行精确的刻画，因此提出了一种用直线段去近似掌纹线的方法，利用线段Hausdorff距离方法去匹配这些线段集，并对其进行了改进。实验的结果表明了该方法的有效性。     

基于移动设备的掌纹验证系统

针对掌纹识别在智能移动设备上的应用,提出一种掌纹识别算法,并嵌入到联想ET980智能手机,开发基于移动设备的掌纹验证系统。以新的方式采集掌纹图像并给出相应的预处理算法。使用优化的Gabor判别方法提取掌纹特征,减少特征提取的时间。系统验证一张掌纹图像的时间小于1 s,同时可获得EER=3.89%的识别精度,满足验证系统的实时性和精度要求。     

基于混合滤波LBP和PCA的掌纹识别

针对基于局部二值模式(LBP)的掌纹识别易受噪声影响,导致算法鲁棒性下降,提出一种结合混合滤波LBP(HFLBP)和主成分分析(PCA)的特征提取方法.从滤波和特征提取的角度对传统LBP算法进行改进,先对图像进行去噪处理,然后对掌纹图像进行分块,提取LBP直方图特征向量,并通过PCA算法对特征向量进行降维,最后利用欧氏距离匹配.在香港理工大学PolyU图库和PolyU噪声图库上与几种典型算法进行对比实验,实验结果表明,本文算法分别获得最低等误率为1.1405％、4.0101％,有效地提高了识别率和鲁棒性,具有很好的应用前景.     

基于DCT鉴别分析的掌纹特征提取

离散余玄变换是一种经典的图像处理技术，而鉴别分析是一种常用的图像特征提取技术。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。该方法首先对于掌纹的离散余玄变换图像，提出了一个二维可分性判据来选择具有良好可分性的频段;然后提出了一种改进的费舍脸方法来提取鉴别特征。在掌纹图象公共数据库上的实验结果验证了本文所提出的方法的有效性。     

非接触式掌纹图像采集与图像增强技术研究

掌纹识别是生物特征识别领域的前沿课题,其中非接触式的掌纹识别技术因其具有用户体验性好、无卫生污染等特点,Et益成为相关研究领域的热点。但是非接触式的掌纹识别技术易受背景复杂、光照不足等不良因素的影响,给掌纹的图像采集与特征的提取匹配带来了困难。为了更好地解决这些问题,需要采用更为有效的图像增强技术。文中介绍了非接触式图像采集与预处理过程中图像增强技术的基本概念,对该技术实现方法进行了归类阐述和分析,探讨了该技术的发展趋势。     

Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes.     

Online Palmprint Identification System for Civil Applications

In this paper, a novel biometric identification system is presented to identity a person's identity by his/her palmprint. In contrast to existing palmprint systems for criminal applications, the proposed system targets at the civil applications, which require identifying a person in a large database with high accuracy in real-time. The system is constituted by four major components: User Interface Module, Acquisition Module, Recognition Module and External Module. More than 7,000 palmprint images have been collected to test the performance of the system. The system can identify 400 palms with a low false acceptance rate, 0.02%, and a high genuine acceptance rate, 98.83%. For verification, the system can operate at a false acceptance rate, 0.017% and a false rejection rate, 0.86%. The execution time for the whole process including image collection, preprocessing, feature extraction and matching is less than 1 second.     

加权自适应CS-LBP与局部判别映射相结合的掌纹识别方法

提取掌纹的最佳低维分类特征一直是掌纹识别研究领域的一个重要方向。针对掌纹图像具有丰富的纹理特征特点,提出一种基于加权自适应中心对称局部二值模式(WACS-LBP)与局部判别映射(LDP)相结合的掌纹识别方法。首先将掌纹感兴趣(ROI)图像分成大小均匀的小区域,利用自适应中心对称局部二值模式(ACS-LBP)算法获取不同区域的纹理特征直方图和权值,经过加权连接得到ROI的加权纹理特征直方图向量;再利用LDP算法对得到的特征向量进行维数约简;最后利用K-最近邻分类器进行掌纹识别。在掌纹公开数据库上进行实验,正确识别率高达97%以上。实验结果表明,该方法不仅是有效、可行的,而且研究思路比较明确。     

一种基于形态小波的在线掌纹的线特征提取方法

在线掌纹中的线特征性能比较稳定，表示方法简单，特征空间小，可实现快速检索与匹配，该文针对在线掌纹图像噪声强，对比度低的特点，提出利用形态中值小波对原努图像进行非线性多分辨率分析，然后取其中的低频分量进行方向选择模板匹配，得到了较理想的实验结果。     

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。