Design of alignment-free cancelable fingerprint templates via curtailed circular convolution

Fraudulent use of stolen fingerprint data and privacy invasion by tracking individuals unlawfully with shared or stolen fingerprint data justify the significance of fingerprint template protection. With no a priori fingerprint image registration, alignment-free cancelable fingerprint templates do not suffer from inaccurate singular point detection. In this paper, we propose an effective alignment-free method for constructing cancelable fingerprint templates via curtailed circular convolution. The proposed method features an efficient one-way transform, which protects the input binary string such that it cannot be retrieved from the length-reduced, convolved output vector. The transformed template fulfills the requirements of non-invertibility, revocability and diversity for cancelable fingerprint templates. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method demonstrates satisfactory performance compared to the existing alignment-free cancelable template schemes.     

A pitfall in fingerprint bio-cryptographic key generation

The core of bio-cryptography lies in the stability of cryptographic keys generated from uncertain biometrics. It is essential to minimize every possible uncertainty during the biometric feature extraction process. In fingerprint feature extraction, it is perceived that pixel-level image rotation transformation is a lossless transformation process. In this paper, an investigation has been conducted on analyzing the underlying mechanisms of fingerprint image rotation processing and potential effect on the major features, mainly minutiae and singular point, of the rotation transformed fingerprint. Qualitative and quantitative analyses have been provided based on intensive experiments. It is observed that the information integrity of the original fingerprint image can be significantly compromised by image rotation transformation process, which can cause noticeable singular point change and produce a non-negligible number of fake minutiae. It is found that the quantization and interpolation process can change the fingerprint features significantly without affecting the visual image. Experiments show that up to 7% bio-cryptographic key bits can be affected due to this rotation transformation.     

Alignment-free cancelable fingerprint template design: A densely infinite-to-one mapping (DITOM) approach

Registration-based cancelable template schemes rely on accurate fingerprint image alignment, which is very difficult to achieve. In this paper, by exploiting pair-minutiae vectors, we develop a lightweight, alignment-free scheme for generating cancelable fingerprint templates. The proposed mathematical model is based on a densely infinite-to-one mapping (DITOM) aiming to achieve the non-invertible property. The transformation designed describes the intersection of a collection of hyperplanes and effectively realizes infinite-to-one mapping. The proposed scheme has the properties of non-invertibility, revocability and multiple template independence. Evaluation of the proposed scheme over FVC2002 DB1, DB2 and DB3 shows that the new method exhibits satisfactory performance compared to existing methods.     

A fast and accurate palmprint recognition system based on minutiae

Palmprint recognition is a challenging problem, mainly due to low quality of the pattern, large nonlinear distortion between different impressions of the same palm and large image size, which makes feature extraction and matching computationally demanding. This paper introduces a high-resolution palmprint recognition system based on minutiae. The proposed system follows the typical sequence of steps used in fingerprint recognition, but each step has been specifically designed and optimized to process large palmprint images with a good tradeoff between accuracy and speed. A sequence of robust feature extraction steps allows to reliably detect minutiae; moreover, the matching algorithm is very efficient and robust to skin distortion, being based on a local matching strategy and an efficient and compact representation of the minutiae. Experimental results show that the proposed system compares very favorably with the state of the art.     

Singular points detection based on multi-resolution in fingerprint images

The singular points of fingerprints, namely core and delta, play an important role in fingerprint recognition and classification systems. Several traditional methods have been proposed; however, these methods cannot achieve the reliable and accurate detection of poor-quality fingerprints. In this paper, an algorithm is proposed which combines improved Poincaré index and multi-resolution analysis to detect singular points. Conventional Poincaré index method is improved on the basis of the Zero-pole Model analysis to detect singular points with different resolutions. A model is presented to extract the multi-resolution information of the fingerprint pattern; this model divides fingerprint image into nonoverlapping blocks corresponding to different block sizes on the basis of wavelet functions to compute multiple resolution directional fields, and block position shifting is performed on these resolution levels to capture the features of the ridge direction patterns, where the corresponding shifting intervals are based on Sampling theorem. The relationship of singularities detected by improved Poincaré index in different resolution directional fields is used to confirm singular points accurately and reliably. The combination of local and global information makes our algorithm more robust to noise than methods that use local information only, and the existence of this algorithm increases the insight into the nature of singular points extraction. The accuracy and reliability of the method are demonstrated by experiment on database NIST-4, public fingerprint databases FVC02 DB1 and DB2.     

基于奇异值分解和小波变换的抗几何失真数字水印新方法

目前,大多数鲁棒图像水印所面临的最大问题就是几何攻击,而现有水印技术大都难以抵抗几何变换类攻击,如旋转、尺度变换等,由于几何攻击破坏了水印分量的同步,即使微小幅度的图像旋转或尺度变换都可能导致水印检测过程失败.提出了一种基于奇异值分解和小波变换的方法来盲提取受到几何攻击后的图像中的水印,即利用了奇异值对几何失真的稳健性,又利用了小波变换对一般攻击的稳健性.实验证明,提出的方法对几何攻击具有很好的鲁棒性.     

指纹识别的预处理组合算法

提出了基于方向分割的一套完整的指方预处理组合算法，主要包括方向图的计算、平滑、方向滤波，局域自适应、二值化，去噪和细化等算法，能很好地增强指纹图像，减少因指纹旋转及平移因素而造成的误识别，为可靠，准确地实现指纹自识别提供一种可行的方法。     

基于频域分析的指纹奇异点区域提取算法及其DSP实现

本文提出了利用频域分析方法提取指纹奇异点的算法。该算法能在指纹预处理过程中完成奇异点提取,减少了检测奇异点的时间消耗。同时,本文采用TMS320DM642处理器对算法的嵌入式应用进行了研究。实验表明,该算法具有优异的性能。     

Monitoring and diagnosis process of abnormal consumption on smart power grid

Unique and stable reference point is essential for registration and identification in automated fingerprint identification systems. Most existing methods for detecting reference points need to scan the fingerprint image or orientation field pixel by pixel or block by block to confirm a candidate reference point. The inherent complexity of this process makes those methods time-consuming. In this paper, we propose a two-step method to improve the efficiency of detecting reference points by (1) determining the singular point, i.e., the approximate position of the reference point, in a novel fast way; then (2) refining the reference point precisely in the local area of the singular point. In the first step, a walking algorithm is proposed which can walk directly to the singular point without scanning the whole fingerprint image and hence it is extremely fast. Then, in the local area around the singular point, an enhanced method based on mean-shift concept (EMS-based method) is designed to localize the reference point precisely. Experimental results on FVC2000 DB1a and DB2a databases validate that the proposed WEMS (Walking + EMS) method outperforms two state-of-the-art methods in terms of accuracy and efficiency.

     

基于连续分布方向图的指纹奇异点检测

在指纹连续分布方向图(场)的基础上,对经典的PoincaréIndex计算公式进行了改进,提出了一种新的基于连续分布方向图的指纹奇异点检测算法。由于指纹连续分布方向图过渡平滑、自然,既具有很好的连续性、渐变性和抗噪性,又具有较高的精确度;而改进后的PoincaréIndex不仅能精确表示向量场的旋转角度,而且还能精确表示向量场的旋转方向。所以,该算法能够在像素级水平精确定位指纹奇异点(core point和delta point),精确度达到一个像素。在FVC2000、FVC2002和FVC2004的训练指纹库(Set B)以及笔者采集的AFIS2004指纹库(含4000幅指纹)上的实验结果验证了该算法的有效性。     

指纹图象特征提取的新方法

指纹特征的提取在指纹自动识别系统中是一个必不可少的重要环节。指纹特征通常包括指纹奇异点和细节特征点。文章在给出了一种计算指纹方向图的新方法基础上提出了奇异点的提取新方法。实验表明该方法能够准确地提取出指纹的奇异点,并具有较强的抗干扰性。针对指纹图象质量较差时,存在大量的伪细节特征点,文章提出了一种新的细节特征点验证的方法,获得了良好实验结果。     

基于差分网格的抗RSD攻击盲指纹方案

为了提高数字指纹的抗综合攻击能力,构造了抗旋转、缩放、扭曲攻击(简记为RSD攻击)的数字指纹嵌入和提取策略。设计了数字指纹的空域—DCT域联合嵌入方案,给出了差分特征点的构造方法,以及基于差分特征点网格的数字指纹嵌入与提取算法,设计了高精度攻击参数辨识算法。实验结果表明,所提方案攻击参数辨识精度达到亚像素级,能抵抗缩放系数大于0.5的缩放攻击,45°角以内的任意旋转攻击,以及25°角以内的任意扭曲攻击;且指纹提取效果并不因旋转角度的增大而降低,也不因扭曲角度的增大而有明显降低。该方案提高了数字指纹的鲁棒性,使数字指纹系统在能抵抗去除攻击,剪切、平移、粘贴攻击(简称CTP攻击)的同时具备抵抗RSD攻击的能力。     

基于Hermite滤波器的指纹增强算法

指纹增强的目的在于改善指纹图像的质量,以提高指纹识别系统的性能。提出了一种基于Hermite滤波器的指纹增强算法,算法利用经典的梯度公式法计算纹线方向,利用指纹图像子块的频谱分布特征计算纹线频率。以纹线方向和频率作为滤波器的主要参数,利用具有良好带通特性的Hermite滤波器和具有可变角度带宽的低通滤波器进行滤波,有效地提高指纹的纹理清晰度,较好地避免奇异点区域的块效应。实验结果表明,算法具有良好的图像增强效果。     

指纹图像方向提取方法的研究

指纹自动识别是图像处理技术、模式识别技术与计算机数据库技术的综合应用。指纹图像的方向信息对指纹的预处理、增强、分类及匹配有着重要的作用。因此，在指纹自动识别系统中具有重要的研究价值。文中在金字塔表示法的基础上提出了一种新的指纹图像方向提取方法。该方法通过改进局部方向的估计方法、确定传递权值等方式，提高指纹图像提取方向信息的准确性。实验表明，这种方法对指纹图像噪声有很好的健壮性，且在质量较差的指纹上提取方向信息的准确性方面相对于常用的局部梯度算法来说有明显提高。     

无方向的三角形匹配指纹识别

目的 指纹匹配是自动指纹识别系统研究的核心内容之一,匹配算法的好坏直接影响识别系统的效能。目前,大多数点模式匹配算法都依赖于指纹方向场的求取,由于输入的指纹图像存在平移、旋转和尺度变化,因此同一个手指在不同时间获得的指纹图像的方向场是不同的,这不仅增加了计算量,也影响了指纹识别的精度。针对上述问题,提出了无方向的三角形匹配算法。方法 提出的三角形匹配算法是以平面中任意点与一个确定的三角形之间的位置结构稳定性为理论基础的。首先,分别在待识指纹图像和模板指纹图像中确定基准三角形;其次,将各个特征点与基准三角形三个顶点的距离组成有序三数组;最后,利用数组的相等程度对指纹相似度进行匹配判断。结果 采用国际标准测试库FVC2004进行综合性能比对实验,实验结果表明,与其他几种匹配算法相比,本文方法在识别精度上提高了27.97%~33.81%,在比对时间上降低了3%~5%,在不同旋转角度下误匹配率平均降低了约86.63%,对噪声、平移、旋转和形变有足够的适应能力,具有较高的容错能力和鲁棒性。结论 无方向的三角形匹配算法是一种全局模式的算法,该算法不受指纹图像方向及其位置的影响,实现过程简单,识别精度高,平均比对时间少,适用于处理不同类型的图像数据。     

A novel hierarchical fingerprint matching approach

Fingerprint matching is an important and essential step in automated fingerprint recognition systems (AFRSs). The noise and distortion of captured fingerprints and the inaccurate of extracted features make fingerprint matching a very difficult problem. With the advent of high-resolution fingerprint imaging techniques and the increasing demand for high security, sweat pores have been recently attracting increasing attention in automatic fingerprint recognition. Therefore, this paper takes fingerprint pore matching as an example to show the robustness of our proposed matching method to the errors caused by the fingerprint representation. This method directly matches pores in fingerprints by adopting a coarse-to-fine strategy. In the coarse matching step, a tangent distance and sparse representation-based matching method (denoted as TD-Sparse) is proposed to compare pores in the template and test fingerprint images and establish one-to-many pore correspondences between them. The proposed TD-Sparse method is robust to noise and distortions in fingerprint images. In the fine matching step, false pore correspondences are further excluded by a weighted RANdom SAmple Consensus (WRANSAC) algorithm in which the weights of pore correspondences are determined based on the dis-similarity between the pores in the correspondences. The experimental results on two databases of high-resolution fingerprints demonstrate that the proposed method can achieve much higher recognition accuracy compared with other state-of-the-art pore matching methods.     

基于Gaussian-Hermite矩的指纹奇异点定位

在指纹分类和识别算法中,提取的奇异点(core点和delta点)数目和奇异点的准确位置是非常重要的.介绍了一种基于Gaussian-Hermite矩分布属性的自适应指纹奇异点定位方法,为了准确地确定奇异点,用到了指纹图像在多种尺度下的不同阶Gaussian-Hermite矩分布,并用一种基于主分量分析(principal component analysis,简称PCA)的方法分析指纹图像的Gaussian-Hermite矩分布.实验结果表明,该算法能够准确地确定奇异点位置.     

Reversible data hiding using side-match predictions on steganographic images

Information hiding is an important method to achieve multi-media security. Recently, many researchers have paid attention to the reversible data hiding scheme, which can completely recover original multi-media files after the embedded data are extracted. In this paper, a side-match approach is proposed to achieve more capacity in histogram-based reversible data hiding for grayscale images. The histogram is created by exploiting the difference in all the values between pixels and their predictedive values. Experimental results show that our method is capable of providing a great embedding capacity without causing noticeable distortion. In one-level hiding, where it has the best capacity, our method conserves image qualities larger than 48 dB. Furthermore, in multilevel hiding, a rotation strategy is proposed to further improve image qualities. Experimental results show that our method performs better than other existing methods in multilevel hiding cases.     

指纹自动识别中图像分割算法研究

为了可靠、准确地实现指纹的自动识别,提出了一种简便、可行的指纹图像的分割算法。通过合理地运用指纹图像的灰度特性,以较低的计算代价有效地解决了指纹图像的分割问题,从而使算法的处理效果好、运行速度快。实验表明,这种分割算法对于指纹图像的预处理十分有效。