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.     

Feed-forward content based image retrieval using adaptive tetrolet transforms

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.     

Efficient fingerprint search based on database clustering

Fingerprint identification has been a great challenge due to its complex search of database. This paper proposes an efficient fingerprint search algorithm based on database clustering, which narrows down the search space of fine matching. Fingerprint is non-uniformly partitioned by a circular tessellation to compute a multi-scale orientation field as the main search feature. The average ridge distance is employed as an auxiliary feature. A modified K-means clustering technique is proposed to partition the orientation feature space into clusters. Based on the database clustering, a hierarchical query processing is proposed to facilitate an efficient fingerprint search, which not only greatly speeds up the search process but also improves the retrieval accuracy. The experimental results show the effectiveness and superiority of the proposed fingerprint search algorithm.     

Intelligent fingerprint quality analysis using online sequential extreme learning machine

Because the quality of fingerprints can be degraded by diverse factors, recognizing the quality of fingerprints in advance can be beneficial for improving the performance of fingerprint authentication systems. This paper proposes an effective fingerprint quality analysis approach based on the online sequential extreme learning machine (OS-ELM). The proposed method is based not only on basic fingerprint properties, but also on the physical properties of the various sensors. Instead of splitting a fingerprint image into traditional small blocks, direction-based segmentation using the Gabor filter is used. From the segmented image, a feature set which consists of four selected independent local or global features: orientation certainty, local orientation quality, consistency, and ridge distance, is extracted. The selected feature set is robust against various factors responsible for quality degradation and can satisfy the requirements of different types of capture sensors. With the contribution of the OS-ELM classifier, the extracted feature set is used to determine whether or not a fingerprint image should be accepted as an input to the recognition system. Experimental results show that the proposed method performs better in terms of accuracy and time consumed than BPNN-based and SVM-based methods. An obvious improvement to the fingerprint recognition system is achieved by adding a quality analysis system. Other comparisons to traditional methods also show that the proposed method outperforms others.     

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。     

Learning Robust Similarity Measures for 3D Partial Shape Retrieval

In this paper, we propose a novel approach to learning robust ground distance functions of the Earth Mover’s distance to make it appropriate for quantifying the partial similarity between two feature-sets. First, we define the ground distance as a monotonic transformation of commonly used feature-to-feature base distance (or similarity) measures, so that in computing the Earth Mover’s distance, the algorithm could better turn its focus on the feature pairs that are correctly matched, while being less affected by irrelevant ones. As a result, the proposed method is especially suited for 3D partial shape retrieval where occlusion and clutter are serious problems. We prove that when the transformation satisfies certain conditions, the metric property of the base distance is sufficient to guarantee the ground distance is a metric (and so is the Earth Mover’s distance), which makes fast shape retrieval on large databases technically possible. Second, we propose a discriminative learning framework to optimize the transformation function based on the real Adaboost algorithm. The optimization is performed in the space of the piecewise constant approximations of the transformation without making any parametric assumption. Finally, extensive experiments on 3D partial shape retrieval convincingly demonstrate the effectiveness of the proposed techniques.     

Pixel-level singular point detection from multi-scale Gaussian filtered orientation field

Singular point, as a global feature, plays an important role in fingerprint recognition. Inconsistent detection of singular points apparently gives an affect to fingerprint alignment, classification, and verification accuracy. This paper proposes a novel approach to pixel-level singular point detection from the orientation field obtained by multi-scale Gaussian filters. Initially, a robust pixel-level orientation field is estimated by a multi-scale averaging framework. Then, candidate singular points in pixel-level are extracted from the complex angular gradient plane derived directly from the pixel-level orientation field. The candidate singular points are finally validated via a cascade framework comprised of nested Poincare indices and local feature-based classification. Experimental results over the FVC 2000 DB2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel-level singular point detection. The experimental results exhibit a low computational cost with better performance. Thus, the proposed method can be employed in real-time fingerprint recognition.     

基于贝叶斯方法的范例检索

提出了利用贝叶斯概率理论进行范例推理，因为在范例库中进行范例检索是范例推理中的一个非常重要的组成部分。该文中主要考虑的是如何利用贝叶斯方法进行范例检索并提出了一个评估两个范例相似程度的匹配函数。在传统的方法中，可以用欧氏距离等方法来评估两个范例的相似性。在实验中，把这两种方法进行了比较，实验结果表明贝叶斯方法不仅呵行，而且比用欧氏距离方法更优。     

低质量指纹图像方向场提取

指纹方向场提取是自动指纹识别系统中的重要环节,是解决自动指纹识别中的某些关键技术的基础.提出一种通过线性投影分析和在sin域中用加权低阶2维勒让德多项式拟合提取指纹方向场的方法;能够解决大噪声低质量指纹图像方向场难以准确提取的问题;在FVC 2000指纹数据库中的大量实验结果表明,与已有基于梯度的指纹方向场估计算法相比,本文方法有更好的提取精度和鲁棒性,对于大噪声的低质量指纹图像能给出很好的方向场估计.     

基于压缩感知和音频指纹的固定音频检索方法

针对现有音频检索中样本音频特征库数据量较大且检索速率慢问题, 本文提出一种基于压缩感知和音频指纹降维的固定音频检索方法. 在音频检索的训练阶段, 首先, 对样本音频信号进行稀疏化处理, 并通过压缩感知算法对稀疏化后的音频数据进行压缩; 其次, 提取压缩信号的音频指纹; 再次, 引入音频指纹离散基尼系数通过计算音频指纹各维度的离散基尼系数对指纹实施降维, 最终得到检索特征库. 在音频检索阶段用和训练阶段相同的算法提取待检音频的特征与音频特征库数据匹配得出检索结论. 实验结果表明, 所提音频检索方法在确保较好的检索准确率的基础上, 大幅度减小了样本音频数据库的存储量, 提高了音频的检索速率.     

低质量指纹图像方向场提取方法

指纹方向场提取是自动指纹识别系统中的重要环节,是解决自动指纹识别中的某些关键技术的基础。提出了一种通过线性投影分析和在sin域中用加权低阶二维勒让德多项式拟合提取指纹方向场的方法;解决了大噪声低质量指纹图像方向场难以准确提取的问题;在FVC 2000指纹数据库中的大量实验表明,与已有基于梯度的指纹方向场估计算法相比,该方法有更好的提取精度和鲁棒性,对于大噪声的低质量指纹图像能给出很好的方向场估计。     

基于投影信号谱分析的指纹纹线间距估计方法

准确估计指纹纹线间距对指纹图像分割、增强及指纹细节特征提取具有重要意义。针对现有纹线间距估计方法存在的不足,提出了一种基于纹线方向投影信号谱分析的指纹纹线间距估计方法。该方法首先对指纹块方向图进行选择性平滑以滤除噪声干扰,然后计算方向投影信号的傅立叶变换能量谱,并通过谱分析提取信号基频,以此估计出纹线间距。实验结果表明,与现有的一些典型方法相比,该文方法对于纹线间距的估计更为准确、可靠。     

一种基于结构匹配的指纹匹配算法

提出了一种基于结构匹配的指纹匹配算法,不仅考虑了指纹的全局特性而且利用了指纹局部结构的平移和旋转不变性。该算法可克服在采集指纹图像时由于非线性形变所引起的细节点的位置、细节点的方向及指纹图像中细节点之间距离改变产生的影响,降低了匹配算法的拒识率。试验结果表明所提出的方法确实提高了指纹匹配算法的有效性。     

Similarity learning for texture image retrieval using multiple classifier system

Multiple Classifier System has found its applications in many areas such as handwriting recognition, speaker recognition, medical diagnosis, fingerprint recognition, personal identification and others. However, there have been rare attempts to develop content-based image retrieval (CBIR) system that uses multiple classifiers to learn visual similarity. Texture as a primitive visual content is often used in many important applications (viz. Medical image analysis and medical CBIR system). In this paper, a texture image retrieval system is developed that learns the visual similarity in terms of class membership using multiple classifiers. The way proposed approach combines the decisions of multiple classifiers to obtain final class memberships of query for each of the output classes is also a novel concept. A modified distance that is weighted with the membership values obtained through similarity learning is used for ranking. Three different algorithms are proposed for the retrieval of images against a query image displaying the strength of multiple classifier approach, class membership score and their interplay to achieve the objective defined in terms of simplicity, retrieval effectiveness and speed. The proposed methods based on multiple classifiers achieve higher retrieval accuracy with lower standard deviation compared to all the competing methods irrespective of the texture database and feature set used. The multiple classifier retrieval schemes proposed here is tested for texture image retrieval. However, these can be used for any other challenging retrieval problems.     

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。     

一种基于多特征融合的彩色图像检索方法

提出了一种基于特征融合的图像检索方法。利用图像的HSV直方图特征建立图像颜色直方图,并采用直方图二次式距离公式取得图像相似性度量值;利用图像的纹理特征建立256维的LBP特征向量,并利用欧式距离取得相似性度量值;通过两特征融合的方法取得图像检索中关键图和检索图之间的相似度值,使得检索取得更好的效果。实验表明,在查准率和...     

A fingerprint retrieval system based on level-1 and level-2 features

This paper proposes a novel fingerprint retrieval system that combines level-1 (local orientation and frequencies) and level-2 (minutiae) features. Various score- and rank-level fusion strategies and a novel hybrid fusion approach are evaluated. Extensive experiments are carried out on six public databases and a systematic comparison is made with eighteen retrieval methods and seventeen exclusive classification techniques published in the literature. The novel approach achieves impressive results: its retrieval accuracy is definitely higher than competing state-of-the-art methods, with error rates that in some cases are even one or two orders of magnitude smaller.     

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。     

基于方向滤波的指纹图像增强算法研究

论文在传统指纹图像增强算法的基础上,提出了一种改进的方向滤波指纹图像增强算法,算法速度得到提高,指纹图像增强效果显著。算法以方向滤波为基础,通过Sobel算子计算指纹图像的梯度和方向,根据指纹的特点设计方向滤波矩阵,由指纹局部方向选择相应方向滤波矩阵进行滤波,使指纹图像在纹线方向上得到增强,在垂直纹线方向上得到减弱,从而增加了前景和背景的对比度,消弱了噪声干扰,突出了指纹的有效信息,为后续的处理过程提供了良好的图像基础。