A variational formulation for fingerprint orientation modeling

Orientation pattern is an important feature for characterizing fingerprint and plays critical roles in fingerprint recognition and fingerprint classification. This paper proposes a framework for modeling the fingerprint orientation field based on the variational principle, where the orientation pattern can be estimated through solving the associated Euler–Lagrange equation. Compared with existing methods, our proposed method has the following features. Firstly, it does not require any prior information about the structure of the acquired fingerprint, such as location of singular point(s). Secondly, it explicitly provides freedom for modeling the singularity in the orientation field. Thirdly, it has less number of parameters. Comparison has been made with respect to state-of-the-arts in fingerprint orientation modeling in terms of modeling accuracy, fingerprint enhancement and singular point detection. Advantages of the proposed method are demonstrated.     

Singular points detection based on zero-pole model in fingerprint images

An algorithm is proposed which combines Zero-pole Model and Hough Transform(HT) to detect singular points. Orientation of singular points is defined on basis of Zero-pole Model which can further explain the practicability of Zero-pole Model. Contrary to orientation field generation, detection of singular points is simplified to determine the parameters of Zero-pole Model. HT uses rather global information of fingerprint images to detect singular points. This makes our algorithm more robust to noise than methods which only use local information. As Zero-pole Model may have a little warp from actual fingerprint orientation field, Poincare index is used to make position adjustment in neighborhood of the detected candidate singular points. Experimental results show that our algorithm performs well and fast enough for real time application in database NIST-4.     

一种鲁棒的指纹奇异点检测方法

提出一种两阶段的奇异点检测方法。将指纹图像分块,求出各块的方向构成块方向图,并在块方向图的基础上利用邻域方向的分布分析结合改进的Poincare Index方法来确定奇异点所在的候选区域,对候选区域中的像素再通过计算局部方向变化率来确定奇异点的精确位置。将此方法用于对FVC2004 DB1_A指纹数据库的图像,实验结果表明这种方法对指纹图像中的噪声有很好的鲁棒性,并且计算简单快速,易于实现。     

Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates.     

一种用于指纹方向场估计的网格插值模型

指纹方向场的估计是指纹识别预处理算法中的重要环节,对算法识别效率起到关键作用。本文提出了一种网格插值模型,该模型以指纹奇异点为中心,将指纹平面做网格划分,利用插值算法建立了方向场与指纹奇异点之间的非线性关系。模型中利用了指纹的全局信息来调整网格点的值,使得它与传统的基于局部信息的方向场算法有本质的区别。在FVC2002 和FVC2004 指纹数据库上的实验结果表明,该模型比传统算法具有更高的准确性和鲁棒性,同时对于低质量的指纹图像,仍然能够给出很好的方向场估计。     

High-speed maneuvering target detection approach based on joint RFT and keystone transform

Increasing the integration time is an effective method to improve small maneuvering target detection performance in radar applications.However,range migration and Doppler spread caused by maneuvering target motion during the integration time make it difficult to improve the coherent accumulation of target’s energy and detection performance.In this study,a new method based on Radon Fourier transform(RFT) and keystone transform(KT) for high-speed maneuvering target detection is proposed.The proposed algorithm utilizes second-order KT to correct the range curvature,and the improved dechirping method to compensate for the Doppler spread.RFT is then used to correct the range walk for target coherent detection.The method is capable of correcting the range migration and the time-varied Doppler frequency of the target without knowing its velocity and acceleration.The advantage of the proposed method is that it can increase the coherent integration time and improve detection performance under the condition of Doppler frequency ambiguity.Compared with the second-order RFT algorithm,the computational burden of the proposed method is greatly reduced under the premise that the two methods have similar estimation accuracy of range,velocity and acceleration.Numerical experiments demonstrate the validity of the proposed algorithm.     

基于复数滤波的指纹奇异点提取方法

在身份识别中,指纹具有唯一性,人的指纹包含大量信息,识别指纹图像很重要.针对指纹图像奇异点中准确判断和精确定位的难题,提出了一种改进的指纹奇异点提取方法.首先,对指纹平方复数点方向场进行多尺度滤波,在平滑噪声的同时,保留奇异区方向的细节信息.对方向场进行复数滤波,并运用启发式规则增强复数滤波的响应幅度,通过分析响应的幅度检测奇异点的位置,由响应的相位确定方向.在FVC 2000上进行实验.结果表明,上述方法可精确提取指纹图像中各奇异点的位置及其方向信息,证明优于与其它方法,结果具有较好的鲁棒性.     

基于奇异点的字典模型指纹方向场去噪方法*

方向场估计是指纹识别过程中非常重要的步骤。传统方法如基于梯度的方法等在处理潜指纹图像时很容易受噪音干扰,而最近提出的基于字典模型的方法无法解决“真词错误”的问题。针对上述问题,本文提出一种融合了零极点模型的字典模型的指纹方向场去噪方法,即将指纹方向场看做是零极点控制的方向场和平滑的残差方向场相叠加的结果,通过首先用零极点模型生成正确的零极点控制的方向场,然后用字典模型修正残差方向场方向场,最后将零极点模型生成的方向场与去噪后的残差方向场融合形成重建方向场,通过基于奇异点的字典模型,我们解决了“真词错误”的问题。为了验证算法的有效性,在NIST SD27潜指纹图像数据库上进行了实验。实验结果表明：对于潜指纹,本文算法能获得比字典模型更精确的方向场,继而可以更好地增强潜指纹图像,并在后续的匹配实验中取得更好的结果。     

指纹奇异点精确定位新方法

针对指纹图像奇异点快速精确定位的难题,提出一种简单实用算法。对指纹图像预处理,计算方向场并归域化,接着选出奇异点候选区,并以Poincare Index（PI）算法从中提取奇异点候选点集。对候选奇异点集去伪并精确定位。采用FVC2004指纹库进行实验验证,结果与PI算法对比,该算法鲁棒性更好,定位更精确,漏检率和误检率分别降低5.86%、6.8%,平均速度提高了3.71~9.38倍,基本满足高精度高速度的指纹奇异点定位要求。     

A Novel Algorithm for Detecting Singular Points from Fingerprint Images

Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this paper, we propose a novel algorithm for singular points detection. After an initial detection using the conventional Poincaré Index method, a so-called DORIC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Experimental results show that our algorithm is accurate and robust, giving better results than competing approaches. The proposed detection algorithm can also be used for more general 2D oriented patterns, such as fluid flow motion, and so forth.     

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.     

Fingerprint classification based on decision tree from singular points and orientation field

In this study, a high accuracy fingerprint classification method is proposed to enhance the performance in terms of efficiency for fingerprint recognition system. The recognition system has been considered as a reliable mechanism for criminal identification and forensic for its invariance property, yet the huge database is the key issue to make the system obtuse. In former works, the pre-classifying manner is an effective way to speed up the process, yet the accuracy of the classification dominates the further recognition rate and processing speed. In this paper, a rule-based fingerprint classification method is proposed, wherein the two features, including the types of singular points and the number of each type of point are adopted to distinguish different fingerprints. Moreover, when fingerprints are indistinguishable, the proposed Center-to-Delta Flow (CDF) and Balance Arm Flow (BAF) are catered for further classification. As documented in the experimental results, a good accuracy rate can be achieved, which endorses the effectiveness of the fingerprint classification scheme for the further fingerprint recognition system.     

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。     

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.     

Residual orientation modeling for fingerprint enhancement and singular point detection

This paper presents a novel method for fingerprint orientation modeling, which executes in two phases. Firstly, the orientation field is reconstructed using a lower order Legendre polynomial to capture the global orientation pattern in the fingerprint structure. Then the preliminary model around the region with presence of fingerprint singularities is dynamically refined using a higher order Legendre polynomial. The singular region is automatically detected through the analysis on the orientation residual field between the original orientation field and the orientation model. The method does not require any prior knowledge on the fingerprint structure. To validate the performance, the method has been applied to fingerprint image enhancement, fingerprint singularity detection and fingerprint recognition using the FVC 2004 data sets. Compared with the recently published Legendre polynomial model, the proposed method attains higher accuracy in fingerprint singularity detection, lower error rates in fingerprint matching.     

Systematic methods for the computation of the directional fieldsand singular points of fingerprints

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint database     

快速鲁棒的城市场景分段平面重建

对于基于图像的城市场景重建,由于光照变化、透视畸变、弱纹理区域等因素的影响,传统像素级与区域级的重建算法通常难以获得可靠的重建结果.为了解决此问题,本文提出一种快速、鲁棒的分段平面重建算法.根据城市场景结构特征与分段平面假设,本文算法首先利用基于连通域检测的空间平面拟合方法从初始空间点中抽取充分且可靠的候选空间平面,然后在MRF（Markov random field）能量最小化框架下将场景的完整结构推断问题转化为平面标记问题进行求解.由于候选平面集与融合灰度一致性度量、空间几何与可见性约束的能量模型的高可靠性,场景的完整结构因此可被有效地重建.实验结果表明,本文算法能较好地克服传统算法可靠性差、重建场景不完整等缺点,同时具有较高的计算效率.     

基于方向的指纹奇异点提取

准确、可靠地检测指纹奇异点(核心点和三角点)对于指纹的分类和匹配有重要的意义。针对指纹图像奇异点提取中准确判断和精确定位的难题,介绍了一种比较好的奇异点检测算法。根据奇异点的性质,利用Poincare Index方法求出核心点和三角点。根据相关规则,清除虚假奇异点。实验结果证明该方法能够从指纹图像中较精确、可靠地提取出奇异点。用该方法对不同质量的指纹图像进行实验,并与其他方法进行比较,结果表明该方法更加有效、可靠,具有很好的鲁棒性。     

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。