Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points.      

A real-time matching system for large fingerprint databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain     

Euler vector for search and retrieval of gray-tone images.

A new combinatorial characterization of a gray-tone image called Euler Vector is proposed. The Euler number of a binary image is a well-known topological feature, which remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. The Euler vector comprises a 4-tuple, where each element is an integer representing the Euler number of the partial binary image formed by the gray-code representation of the four most significant bit planes of the gray-tone image. Computation of Euler vector requires only integer and Boolean operations. The Euler vector is experimentally observed to be robust against noise and compression. For efficient image indexing, storage and retrieval from an image database using this vector, a bucket searching technique based on a simple modification of Kd-tree, is employed successfully. The Euler vector can also be used to perform an efficient four-dimensional range query. The set of retrieved images are finally ranked on the basis of Mahalanobis distance measure. Experiments are performed on the COIL database and results are reported. The retrieval success can be improved significantly by augmentiong the Euler vector by a few additional simple shape features. Since Euler vector can be computed very fast, the proposed technique is likely to find many applications to content-based image retrieval.     

Deep learning compact binary codes for fingerprint indexing

With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.     

利用去除形变的方法进行指纹匹配

提出了一种基于细节点的指纹匹配方法。定义了一种新的结构邻接特征联合体(AFU),并用这个与旋转和平移无关的局部特征与指纹细节点进行比对;利用纹路的频率和块方向信息对细节点的位置和方向进行重新调整以增加匹配的可靠性。实验结果表明该方法可以很好地处理指纹中出现的形变问题,具有较好的匹配效果。     

指纹图像的预处理算法

根据指纹的固有特性及常用算法在指纹处理中的缺陷，文中提出一套有效的预处理算法。对于图像增强，算法利用指纹的方向特性和频率特性设计出Gabor滤波器对图像进行滤波，取得理想的效果；在图像分割，二值图像处理等方面也作了改进。     

基于颜色和形状特征的彩色图像显示与检索技术

提出了一种有效用于抽取特征、索引和检索彩色图像的技术途径,通过提取图像的颜色不变量,建立相应的色度直方图（hue histogram）来表示图像的颜色分布特征,为了描述图像中对象的位置及方向特征,首先计算图像的色度轮廓并对其进行Radon变换,然后计算相应的“空间直方图”,由此,得到了一种基于图像的颜色分布特征和形状特征的新的图像表示方法,为了计量图像的全局相似度,基于“累积距离”和“基于向量的距离”定义了两种图像的距离度量,并分别讨论了距离度量的选取与归一化、不同子特征的组合等CBIR所涉及的关键问题,实验结果表明,文中提出的方法能获得满意的检索性能,其检索结果能较好地接近于人的视觉感和结果。     

A wavelet based multiresolution algorithm for rotation invariant feature extraction

The present work aims at proposing a new wavelet representation formula for rotation invariant feature extraction. The algorithm is a multilevel representation formula involving no wavelet decomposition in standard sense. Using the radial symmetry property, that comes inherently in the new representation formula, we generate the feature vectors that are shown to be rotation invariant. We show that, using a hybrid data mining technique, the algorithm can be used for rotation invariant content based image retrieval (CBIR). The proposed rotation invariant retrieval algorithm, suitable for both texture and nontexture images, avoids missing any relevant images but may retrieve some other images which are not very relevant. We show that the higher precision can however be achieved by pruning out irrelevant images.     

Spatial Color Indexing Using Rotation,Translation, and Scale Invariant Anglograms

As color plays an essential role in image composition, many color indexing techniques have been studied for content-based image retrieval. This paper examines the use of a computational geometry-based spatial color indexing methodology for effective and efficient image retrieval. In this scheme, an image is evenly divided into a number of M * N non-overlapping blocks, and each individual block is abstracted as a unique feature point labeled with its spatial location and dominant colors. For each set of feature points labeled with the identical color, we construct a Delaunay triangulation and then compute the feature point histogram by discretizing and counting the angles produced by this triangulation. The concatenation of all these feature point histograms serves as the image index, the so-called color anglogram. An important contribution of this work is to encode the spatial color information using geometric triangulation, which is rotation, translation, and scale invariant. We have compared the proposed approach with two of the best performing of recent spatial color indexing schemes, Color-WISE and the color correlogram approaches, respectively, at image block and pixel levels of different granularity. Various experimental results demonstrate the efficacy of our techniques.     

Log-polar wavelet energy signatures for rotation and scale invariant texture classification

Classification of texture images is important in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n /spl middot/ log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.     

Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables

This paper proposes a novel algorithm for reconstructing the fingerprint orientation field (FOF). The basic idea of the algorithm is to reconstruct the ridge orientation by using the best quadratic approximation by orthogonal polynomials in two discrete variables. We first estimate the local region orientation by the linear projection analysis (LPA) based on the vector set of point gradients, and then reconstruct the ridge orientation field using the best quadratic approximation by orthogonal polynomials in two discrete variables in the sine domain. In this way, we solve the problem that is difficult to accurately extract low quality fingerprint image orientation fields. The experiments with the database of FVC 2004 show that, compared to the state-of-the-art fingerprint orientation estimation algorithms, the proposed method is more accurate and more robust against noise, and is able to better estimate the FOF of low quality fingerprint images with large areas of noise.     

Translation invariants of Zernike moments

Moment functions defined using a polar coordinate representation of the image space, such as radial moments and Zernike moments, are used in several recognition tasks requiring rotation invariance. However, this coordinate representation does not easily yield translation invariant functions, which are also widely sought after in pattern recognition applications. This paper presents a mathematical framework for the derivation of translation invariants of radial moments defined in polar form. Using a direct application of this framework, translation invariant functions of Zernike moments are derived algebraically from the corresponding central moments. Both derived functions are developed for non-symmetrical as well as symmetrical images. They mitigate the zero-value obtained for odd-order moments of the symmetrical images. Vision applications generally resort to image normalization to achieve translation invariance. The proposed method eliminates this requirement by providing a translation invariance property in a Zernike feature set. The performance of the derived invariant sets is experimentally confirmed using a set of binary Latin and English characters.     

指纹图像预处理和特征提取

根据指纹的固有规律，提出了一套较完整的指纹图像预处理和特征提取算法。改进了指纹脊线频率求取算法和二值图像去孔噪声算法，并提出了一种新的滤除指纹伪特征点的方法。通过对上百幅不同质量的指纹图像进行测试，获得了较好的效果。     

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.     

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods.     

指纹图像多尺度分类字典稀疏增强

目的 自动指纹识别系统大多是基于细节点匹配的,系统性能依赖于输入指纹质量。输入指纹质量差是目前自动指纹识别系统面临的主要问题。为了提高系统性能,实现对低质量指纹的增强,提出了一种基于多尺度分类字典稀疏表示的指纹增强方法。方法 首先,构建高质量指纹训练样本集,基于高质量训练样本学习得到多尺度分类字典;其次,使用线性对比度拉伸方法对指纹图像进行预增强,得到预增强指纹;然后,在空域对预增强指纹进行分块,基于块内点方向一致性对块质量进行评价和分级;最后,在频域构建基于分类字典稀疏表示的指纹块频谱增强模型,基于块质量分级机制和复合窗口策略,结合频谱扩散,基于多尺度分类字典对块频谱进行增强。结果 在指纹数据库FVC2004上将提出算法与两种传统指纹增强算法进行了对比实验。可视化和量化实验结果均表明,相比于传统指纹增强算法,提出的方法具有更好的鲁棒性,能有效改善低质量输入指纹质量。结论 通过将指纹脊线模式先验引入分类字典学习,为拥有不同方向类别的指纹块分别学习一个更为可靠的字典,使得学习到的分类字典拥有更可靠的脊线模式信息。块质量分级机制和复合窗口策略不仅有助于频谱扩散,改善低质量块的频谱质量,而且使得多尺度分类字典能够成功应用,克服了增强准确性和抗噪性之间的矛盾,使得块增强结果更具稳定性和可靠性,显著提升了低质量指纹图像的增强质量。     

一种起始点无关的小波系数形状匹配

小波变换的多分辨率特征使其在计算机视觉中得到广泛的应用,在形状匹配中,小波变换对起始点的依赖制约了小波变换的应用。为了克服小波变换对起始点的依赖,引入Zernike矩,提出一种起始点无关的小波系数形状匹配算法。对输入图像进行预处理后提取目标轮廓,生成具有平移、尺度不变的形状链状表达,并通过小波变换进行多尺度分析。最后计算各个尺度下的各阶Zernike矩,来解决小波变换的起始点问题,实现形状表达的旋转不变性。实验结果表明该算法适用于轮廓较明显的目标,同时具有速度快、精度高、鲁棒性强的优点。     

Geometric invariant features in the Radon transform domain for near-duplicate image detection

Radon transform has been widely used in content-based image representation due to its excellent geometric properties. In this paper, we propose a family of geometric invariant features based on Radon transform for near-duplicate image detection. According to the theoretical analysis between geometric operations (translation, scaling, and rotation) and Radon transform, we present a geometric invariant feature model. Based on the feature model, we developed four kinds of geometric invariant features. In addition, a uniform sampling technique is introduced to combine different features. The comprehensive performance of the combined feature is better than that of a single one. Extensive experiments show that the proposed features are robust, not only to rotation and scaling, but also to other operations, such as compression, noise contamination, blurring, illumination modification, cropping, etc., and achieve strong competitive performance compared with the state-of-the-art image features.