基于分区模板的重叠指纹分离方法

在刑侦应用中,常需要将现场得到的多个重叠在一起的指纹分离开来。论文提出一种基于分区模板的重叠指纹分离方法。不同类型指纹脊线走向具有特定的模式,定义与每种类型指纹对应的指纹分区模板。这样,将分区模板施用于所对应类型的指纹后,每一分区内的指纹脊线成为走向基本一致的平行曲线。因而采用具有方向和频率选择性的Gabbor滤波器,选择合适的参数,可提取各个区内所要获得的指纹脊线。通过对人工合成图像和现场采集指纹图像的实验结果表明,提出的方案可有效地提取重叠在一起的指纹,保留绝大多数指纹特征点。因此,该文的方案较好地解决了指纹预处理中很少涉及,但在实践中却经常遇到的问题。     

An effective location fingerprint model for wireless indoor localization

A model for predicting precision and accuracy performance of indoor fingerprint based positioning systems is very desirable for system designers as it helps estimate the probability of location selection before actual deployment. Such information can be used to tune the fingerprint database or improve the offline fingerprint collection phase. This paper presents a new analytical model that applies proximity graphs for approximating the probability distribution of error distance given a location fingerprint database using WLANs received signals, and its associated statistics. Simulations are used to validate the analytical model, which is found to produce results close to that from simulations. The model permits an analysis of the internal structure of location fingerprints. We employ the analysis of the fingerprint structure to identify and eliminate inefficient location fingerprints stored in the fingerprint database. Knowledge of where the inefficient fingerprints are can potentially be employed in a better location fingerprint collecting scheme from a grid system in the offline phase.     

On the individuality of fingerprints

Fingerprint identification is based on two basic premises: (1) persistence and (2) individuality. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae features to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of a false correspondence between minutiae-based representations from two arbitrary fingerprints belonging to different fingers. Our results show that (1) contrary to the popular belief, fingerprint matching is not infallible and leads to some false associations, (2) while there is an overwhelming amount of discriminatory information present in the fingerprints, the strength of the evidence degrades drastically with noise in the sensed fingerprint images, (3) the performance of the state-of-the-art automatic fingerprint matchers is not even close to the theoretical limit, and (4) because automatic fingerprint verification systems based on minutia use only a part of the discriminatory information present in the fingerprints, it may be desirable to explore additional complementary representations of fingerprints for automatic matching.     

Prefetch-aware fingerprint cache management for data deduplication systems

Data deduplication has been widely utilized in large-scale storage systems, particularly backup systems. Data deduplication systems typically divide data streams into chunks and identify redundant chunks by comparing chunk fingerprints. Maintaining all fingerprints in memory is not cost-effective because fingerprint indexes are typically very large. Many data deduplication systems maintain a fingerprint cache in memory and exploit fingerprint prefetching to accelerate the deduplication process. Although fingerprint prefetching can improve the performance of data deduplication systems by leveraging the locality of workloads, inaccurately prefetched fingerprints may pollute the cache by evicting useful fingerprints. We observed that most of the prefetched fingerprints in a wide variety of applications are never used or used only once, which severely limits the performance of data deduplication systems. We introduce a prefetch-aware fingerprint cache management scheme for data deduplication systems (PreCache) to alleviate prefetch-related cache pollution. We propose three prefetch-aware fingerprint cache replacement policies (PreCache-UNU, PreCache-UOO, and PreCache-MIX) to handle different types of cache pollution. Additionally, we propose an adaptive policy selector to select suitable policies for prefetch requests. We implement PreCache on two representative data deduplication systems (Block Locality Caching and SiLo) and evaluate its performance utilizing three real-world workloads (Kernel, MacOS, and Homes). The experimental results reveal that PreCache improves deduplication throughput by up to 32.22% based on a reduction of on-disk fingerprint index lookups and improvement of the deduplication ratio by mitigating prefetch-related fingerprint cache pollution.     

Altered fingerprints: analysis and detection

The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has heightened the need for ensuring that these systems are not compromised. While several issues related to fingerprint system security have been investigated, including the use of fake fingerprints for masquerading identity, the problem of fingerprint alteration or obfuscation has received very little attention. Fingerprint obfuscation refers to the deliberate alteration of the fingerprint pattern by an individual for the purpose of masking his identity. Several cases of fingerprint obfuscation have been reported in the press. Fingerprint image quality assessment software (e.g., NFIQ) cannot always detect altered fingerprints since the implicit image quality due to alteration may not change significantly. The main contributions of this paper are: 1) compiling case studies of incidents where individuals were found to have altered their fingerprints for circumventing AFIS, 2) investigating the impact of fingerprint alteration on the accuracy of a commercial fingerprint matcher, 3) classifying the alterations into three major categories and suggesting possible countermeasures, 4) developing a technique to automatically detect altered fingerprints based on analyzing orientation field and minutiae distribution, and 5) evaluating the proposed technique and the NFIQ algorithm on a large database of altered fingerprints provided by a law enforcement agency. Experimental results show the feasibility of the proposed approach in detecting altered fingerprints and highlight the need to further pursue this problem.     

基于隐马尔可夫模型的指纹分类研究

指纹分类是针对大型指纹库的一个重要的索引方式，可以有效地提高指纹匹配的效率．指纹类型的不同表现为指纹纹理结构的差异，而指纹的方向场则可以有效地描述纹理结构的差异．同一类型指纹不同区域上方向角结构的差异以及相邻区域间方向角结构的联系可以视作一个马尔可夫随机场．本文利用嵌入式隐马尔可夫模型对指纹方向场进行建模分析，通过合理地抽取指纹的类型特征，构造观察向量、进行建模训练，然后利用训练好的马尔可夫模型进行匹配，最终提出并实现了一种新的鲁棒性强且精度较高的指纹分类方法．     

An algorithm for distorted fingerprint matching based on local triangle feature set

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel method, a fuzzy feature match (FFM) based on a local triangle feature set to match the deformed fingerprints. The fingerprint is represented by the fuzzy feature set: the local triangle feature set. The similarity between the fuzzy feature set is used to characterize the similarity between fingerprints. A fuzzy similarity measure for two triangles is introduced and extended to construct a similarity vector including the triangle-level similarities for all triangles in two fingerprints. Accordingly, a similarity vector pair is defined to illustrate the similarities between two fingerprints. The FFM method maps the similarity vector pair to a normalized value which quantifies the overall image to image similarity. The proposed algorithm has been evaluated with NIST 24 and FVC2004 fingerprint databases. Experimental results confirm that the proposed FFM based on the local triangle feature set is a reliable and effective algorithm for fingerprint matching with nonlinear distortions.     

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.     

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.     

基于图像插值和椭圆拟合的联指图分割算法

提出一种基于图像插值和椭圆拟合的联指图分割算法。该算法充分利用指纹图像中指纹区域和非指纹区域存在的灰度差异,实现了多指指纹的自动分割。实验表明,该算法不仅能够对低对比度联指图、噪声联指图、"光环"联指图、联指图旋转、部分残缺联指图等常规联指图进行正确分割,且可成功实现指纹连接情况下的特殊联指图分割。     

Latent fingerprint matching

Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.     

基于细节点方向分布的指纹特征匹配

提出了一种新的指纹细节点匹配算法。该算法首先基于细节点方向分布特性将指纹在旋转方向上对齐，减少了大量的旋转操作，然后利用大小变化的界限盒判定细节点之间的对应关系。该算法不受指纹之间旋转变形、平移变换和非线性变形的影响，也不受细节点提取误差的影响。实验结果表明该算法不仅处理速度较快，而且具有令人满意的精度。     

基于梯度矢量的指纹特征检出算法研究

自动指纹识别的关键技术之一是指纹特征的检出。目前,指纹特征检出大多是先采用图像增强和图像分割技术检出指纹纹线,然后从指纹纹线图像中检出指纹特征。用这些方法检出的指纹特征依赖于图像分割的精度,而且检出过程比较复杂,运算速度比较慢。为此,提出一种直接按指纹梯度矢量的方向图进行指纹特征检出的算法。文中的算法是先采用拓宽的Prewitt算子计算指纹图像的梯度矢量图,然后计算梯度矢量的方向一致率,最后由方向一致率图检出指纹特征点。首先介绍方法的基本原理,然后给出了采用提出的方法检出指纹特征的实验例子。     

Global ridge orientation modeling for partial fingerprint identification

Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.     

An application of stochastic languages to fingerprint pattern recognition

The purpose of this paper is to present a stochastic syntactic approach for representation and classification of fingerprint patterns.The fingerprint impressions are subdivided into sampling squares which are preprocessed for feature extraction. First, a brief summary of application of a class of context-free languages for recognition of fingerprints is presented. Next, using the same set of features, a class of stochastic context-free languages was used to further classify the fingerprint impressions. The recognizers using the class of context-free and stochastic context-free languages are named the first-level and second-level classifiers, respectively. Experimental results in terms of real data fingerprints are presented.     

指纹快速匹配算法研究

设计了指纹快速匹配算法。算法以分类时获取的奇异点为参考点对指纹进行定位,利用奇异点特殊邻近结构缩小细节点匹配规模,在可变界限盒的基础上设计合理匹配策略。该方法不仅可以提高计算速度,也能够适应指纹非线性形变的特点,增加算法鲁棒性。在FVC2000数据库上的测试结果显示,该算法在保证自动指纹识别系统识别准确性的同时,还使得指纹辨识速度有了显著的提高。     

基于动态浏览器指纹的链接检测技术研究

随着用户隐私泄露和网络欺诈问题的出现,传统检测机制已经不能阻挡愈演愈烈的攻击行为,需要新的技术手段辅助进行Web安全检测。作为沟通用户与网络数据的桥梁,浏览器广泛应用于用户与各种应用程序之间的交互,其中浏览器指纹包含用户浏览器及设备等多种特征信息,其独特性可以极大地提高用户识别的准确率。但是浏览器指纹随时间不断发生变动,基于静态指纹的研究方案难以满足动态指纹检测的要求。针对这一问题,为了提高动态浏览器指纹的识别和链接检测能力,从多维角度获取参数并进行筛选,得到细粒度和高区分度的特征进行指纹检测,防止过多不必要参数带来的运算代价,并将双向门控循环单元（Bi GRU,bidirectional gating recurrent unit）引入指纹检测工作中,提出了基于堆叠Bi GRU的动态浏览器指纹链接检测模型,解决了指纹检测过程中忽略前后向信息交互导致的检测精度不高的问题,在准确率和链接时长等指标上有了一定的提升。进而针对不同特征参数的重要性不同,赋予相同权重会造成检测误差的问题,结合注意力机制聚焦稳定性更强的指纹特征,提出了基于Att-Bi GRU的动态浏览器指纹链接检测模型,较堆叠B...     

节能灯谐波指纹的测试研究与分析

本文对LED节能灯的谐波发射特性展开研究,建立典型LED内部结构模型,揭示LED谐波发射机理,仿真研究其谐波发射特性。提出谐波指纹的概念,表示谐波源的谐波发射特性,以无源元件对谐波电压的响应,论证谐波指纹特征。搭建实验平台,制定测试计划,对4个品牌的8款LED节能灯（共4种不同功率）在单独使用、串联使用、并联使用的情况下,进行测试,并分析其谐波指纹。测试结果表明,8款LED具有不同的谐波指纹,证明每一个设备都具有特殊的谐波指纹,电气结构相似的设备,具有相似谐波指纹。LED节能灯普遍具有相似的电气结构,测试得到的谐波指纹也呈现成了相似的分布特性。为以谐波指纹为载体,通过对设备电气结构的分析获取设备谐波发射特性奠定了基础。     

一种新的指纹图像分割算法

指纹图像分割是自动指纹识别的重要步骤。对指纹特征和传统指纹分割方法进行了研究,提出了一种新的指纹图像分割算法。先对指纹图像进行裁剪,如果指纹图像对比度较低,则对图像进行均衡化处理;然后对图像进行顶帽变换,补偿不均匀的背景亮度;最后用迭代自组织分析算法(ISODATA聚类)对图像进行分块分割和形态学图像处理。对国际指纹识别竞赛(FVC)数据库不同质量的指纹进行了测试,实验结果表明,此算法对不同质量的指纹是有效的,效果好于传统指纹分割方法。