Fingerprint verification based on minutiae features: a review

Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint-based identification in the early 1960s. More recently, fingerprints have found an application in biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on automated fingerprint matching over the last four decades. In particular, the focus is on minutiae-based algorithms. Minutiae features contain most of a fingerprints individuality, and are consequently the most important fingerprint feature for verification systems. Minutiae extraction, matching algorithms, and verification performance are discussed in detail, with open problems and future directions identified.

Fingerprint classification based on Adaboost learning from singularity features

Fingerprint classification is an important indexing scheme to narrow down the search of fingerprint database for efficient large-scale identification. It is still a challenging problem due to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. In this paper, we presents a fingerprint classification algorithm that uses Adaboost learning method to model multiple types of singularity features. Firstly, complex filters are used to detect the singularities. For powerful representation, we compute the complex filter responses of the detected singularities at multiple scales and a feature vector is constructed for each scale that consists of the relative position and direction and the certainties of the singularities. Adaboost learning method is then applied on decision trees to design a classifier for fingerprint classification. Finally, fingerprint class is determined by the ensemble of the classification results at multiple scales. The experimental results and comparisons on NIST-4 database have shown the effectiveness and superiority of the fingerprint classification algorithm.     

Fingerprint classification based on extraction and analysis of singularities and pseudo ridges

In this paper, we introduce a new approach to fingerprint classification based on extraction and analysis of both singularities and traced pseudo ridges relating to singular points. Because of the image quality, it is difficult to get the correct number and position of the singularities that are widely used in current structural classification methods. With the help of pseudo ridge tracing and analysis of the traced curves, our method does not rely on the extraction of the exact number and positions of the true singular point(s), thus improving the classification accuracy. This method has been tested on the NIST special fingerprint database 4. For the 4000 images in this database, the classification accuracy reaches 92.7% for the 4-class problem.     

Fingerprint classification by a hierarchical classifier

Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.     

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.     

Fingerprint classification using a fuzzy multilayer perceptron

A fuzzy multilayer perceptron is used for the classification of fingerprint patterns. The input vector consists of texturebased features along with some directional features. The output vector is defined in terms of membership values to the three classes, viz.Whorl, Left Loop and Right Loop. Perturbation is produced randomly at pixel locations to generate noisy patterns. This helps to demonstrate the ability of the model in handling distorted fingerprint images. A study is made on the effect of reducing the number of input features while increasing the size of the network on its recognition performance.     

基于Gabor滤波的指纹增强算法

介绍了一种改进的Gabor滤波的指纹图像增强算法。提出一种新的指纹图像分割算法，并对指纹核心区域进行多方向滤波合成。实验证明，该套增强算法运行稳定，效果好，鲁棒性强。     

A Multi-Classifier Approach to Fingerprint Classification

Fingerprint classification is a challenging pattern recognition problem which plays a fundamental role in most of the large fingerprint-based identification systems. Due to the intrinsic class ambiguity and the difficulty of processing very low quality images (which constitute a significant proportion), automatic fingerprint classification performance is currently below operating requirements, and most of the classification work is still carried out manually or semi-automatically. This paper explores the advantages of combining the MASKS and MKL-based classifiers, which we have specifically designed for the fingerprint classification task. In particular, a combination at the ‘abstract level’ is proposed for exclusive classification, whereas a fusion at the ‘measurement level’ is introduced for continuous classification. The advantages of coupling these distinct techniques are well evident; in particular, in the case of exclusive classification, the FBI challenge, requiring a classification error ≤ 1% at 20% rejection, was met on NIST-DB14. Received: 06 November 2000, Received in revised form: 25 October 2001, Accepted: 03 January 2002     

Fingerprint pattern classification

This paper presents a new method for fingerprint classification. In this method, fingerprint images are divided into 32 × 32 subregions to obtain direction pattern. Next, the relaxation smoothing process with singularity detection and convergency checking is performed. Starting from the singular regions found, feature parameters of the fingerprint are obtained by extracting major flow-line¹ traces. The result of the experiments shows that this approach is capable of classifying fingerprint patterns into more than ten categories.     

改进的基于Gabor滤波器的指纹增强算法

研究并实现了利用Gabor滤波器对指纹图像增强的算法。改进了指纹图像方向图和纹线频率的提取方法,增加了对特征点的额外处理,并研究了Gabor函数的具体应用形式。实践表明,该方法能大幅度提高指纹图像质量。     

基于改进的Gabor滤波指纹图像增强算法研究

对Gabor滤波器进行改进,改进的算法利用了指纹图像的局部特性,结合局部四邻城的关联特性,并且采用固定频率来代替复杂的频率计算.实验结果证明,改进的Gabor滤波算法不仅增强了指纹图像信息,同时也提高了处理速度.     

图书馆指纹管理系统中的图像增强算法研究

简单介绍了图书馆指纹管理系统,针对部分读者指纹图像不清晰的问题,采用了图像增强的算法,详细介绍了Gabor滤波器的图像增强算法。实验结果表明,此算法能较好地去除指纹图像中的噪声,达到增强的效果。     

基于Gabor小波核心算法的指纹图像预处理

本文介绍了一种基于小波技术的指纹图像增强算法——Gabor滤波。详细的阐述了Gabor滤波器的构造及其在指纹图像增强中的应用，并从原理上分析了应用中遇到的问题，提出了改进的方法。     

方向场和频率场约束的结构光条纹图像增强算法

条纹图像增强在指纹图像处理与识别、结构光三维重建中有重要应用。针对结构光三维重建的条纹图像处理,提出了一种基于图像方向场与频率场约束的条纹图像增强算法,设计了图像方向场与频率场的计算方法,构造了方向场与频率场的约束的Gabor滤波器。算法首先对条纹图像进行高斯滤波,去除图像亮度不均匀的影响;然后计算条纹图像的方向场与频率场;最后在图像方向场与频率场的约束下对图像进行Gabor滤波。实验结果表明,算法可有效消除图像光照不均匀的影响,较好地增强结构光图像的条纹信息。     

Personal identification based on finger-vein features

Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.     

基于纹理特征的指纹识别算法

基于纹理特征的指纹识别方法,具有计算量小的特点。本文对该指纹识别算法进行了系统研究,提出了一套新颖的方向图修正算法,对于噪声相对较小的情况具有良好的效果。本文对多窗口法求块方向图给出了减少运算量的方法;在方向滤波器设计方面,给出了一个方向滤波器的闭合等式,可以简化方向滤波器设计,大大减少运算量,并取得了较好的增强效果;在中心点求取方面,提出了一种新的粗搜索算法,可以减少计算量,加快中心区域搜索速度。     

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.     

指纹图像增强算法研究

本文根据笔者近年来的研究试验结果，指出了传统指纹图像增强算法的不足，以两种典型滤波算法为主，对近年来流行的基于方向场估计的滤波增强算法进行了分析和评价，并针对这两种算法的不足之处提出了一种新的算法思想。     

一种改进的指纹分类方法

提供一种改进的指纹分类算法,该算法不仅利用了脊的方向信息而且还利用了脊的结构信息和指纹图像中的奇异点,提高了分类的准确度。在此将指纹分为5类：涡型、右旋型、左旋型,弓型,弧型。实验表明这种算法精确度高,结果可靠。     

一种基于大型指纹数据库的检索方法

随着指纹识别应用范围的逐渐扩大,人们对大型指纹数据库的实时性要求也越来越高,因此迫切需要一种有效的索引和检索技术。该文根据指纹数据库自身的特点,提出了一种检索和索引大型指纹数据库的方法。该方法利用了指纹图的全局特征———指纹类别和纹脊密度来建立多级索引,采用集成多级匹配检索方法缩小搜索空间,提高检索速度,取得了很好的效果。