Singular point detection by shape analysis of directional fields in fingerprints

This paper presents a new fingerprint singular point detection method that is type-distinguishable and applicable to various fingerprint images regardless of their resolutions. The proposed method detects singular points by analyzing the shapes of the local directional fields of a fingerprint image. Using the predefined rules, all types of singular points (upper core, lower core, and delta points) can be extracted accurately and delineated in terms of the type of singular points. In case of arch-type fingerprints there exists no singular point, but reference points for arch-type fingerprints are required to be detected for registration. Therefore, we propose a new reference point detection method for arch-type fingerprints as well. The result of the experiments on the two public databases (FVC2000 2a, FVC2002 2a) with different resolutions demonstrates that the proposed method has high accuracy in locating each types of singular points and detecting the reference points of arch-type fingerprints without regard to their image resolutions.     

Direct gray-scale minutiae detection in fingerprints

Most automatic systems for fingerprint comparison are based on minutiae matching. Minutiae are essentially terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. Automatic minutiae detection is an extremely critical process, especially in low-quality fingerprints where noise and contrast deficiency can originate pixel configurations similar to minutiae or hide real minutiae. Several approaches have been proposed in the literature; although rather different from each other, all these methods transform fingerprint images into binary images. In this work we propose an original technique, based on ridge line following, where the minutiae are extracted directly from gray scale images. The results achieved are compared with those obtained through some methods based on image binarization. In spite of a greater conceptual complexity, the method proposed performs better both in terms of efficiency and robustness     

尖拱型指纹图像中奇异点检测方法

指纹图像中的奇异点携带着重要的指纹特征信息,因为有些尖拱型指纹图像中的核心点和三角点相隔很近,以至计算出的Poincare Index值相互抵消,结果接近于0,Poincare Index方法就会将该指纹判为拱型指纹,针对这一问题,对Poincare Index方法进行改进,提出了一种新的奇异点或参考点检测与计算方法。实验结果表明,该方法能很好地解决这一局限性问题。     

Automated lanes detection and comparison of bacterial electrophoretic protein fingerprints using fast Fourier transformation.

A method of computer-automated analysis of bacterial fingerprints produced by electrophoresis of proteins in a one-dimensional slab gel system is described. Proteins were visualized by silver staining. Western blotting, or autoradiography. Gels were recorded with a CCD camera, and after initial manual removal of the unwanted image margins, track margins were identified and extracted and a normalized trace was produced automatically using Fourier routines to smooth plots required for this process. Normalized traces were then compared by Fourier correlation after application of a high-pass step filter.     

Method of detection of real fingerprints on the basis of the Radon transform

An effective approach for the detection of real fingerprints based on the Radon transform is proposed. The results demonstrate that the method well distinguishes false fingers from real ones. The method proposed uses only one image, does not require additional equipment, and is easily integrated into existing fingerprint recognition systems.     

Visualizing substructural fingerprints

Substructural fingerprints have proven very useful for chemical library and diversity analysis, but their high dimensionality makes them poorly suited to principal components analysis and to standard nonlinear mapping methods. By using a combination of optimizable K-dissimilarity selection (OptiSim) and a modified stress function that suppresses effects of distances that fall beyond a characteristic horizon, it is possible to relax principal components analysis coordinates into more consistently meaningful projections from fingerprint space into two dimensions. The nonlinear maps so obtained are useful for characterizing combinatorial libraries, for comparing sublibraries, and for exploring the distribution of biological properties across structural space.     

Type classification of fingerprints: a syntactic approach

A fingerprint classification procedure using a computer is described. It classifies the prints into one of ten defined types. The procedure is implemented using PICAP (picture array processor). The picture processing system includes a TV camera input and a special picture processor. The first part of the procedure is a transformation of the original print to a sampling matrix, where the dominant direction of the ridges for each subpicture is indicated. After smoothing, the lines in this pattern are traced out and converted to strings of symbols. Finally, a syntactic approach is adopted to make the type classification based on this string of symbols.     

Wizard: a database inference analysis and detection system

The database inference problem is a well-known problem in database security and information system security in general. In order to prevent an adversary from inferring classified information from combinations of unclassified information, a database inference analyst must be able to detect and prevent possible inferences. Detecting database inference problems at database design time provides great power in reducing problems over the lifetime of a database. We have developed and constructed a system called Wizard to analyze databases for their inference problems. The system takes as input a database schema, its constituent instances (if available) and additional human-supplied domain information, and provides a set of associations between entities and/or activities that can be grouped by their potential severity of inference vulnerability. A knowledge acquisition process called microanalysis permits semantic knowledge of a database to be incorporated into the analysis using conceptual graphs. These graphs are then analyzed with respect to inference-relevant domains we call facets using tools we have developed. We can determine inference problems within single facets as well as some inference problems between two or more facets. The architecture of the system is meant to be general so that further refinements of inference information subdomains can be easily incorporated into the system     

IDGraphs: intrusion detection and analysis using stream compositing

IDGraphs is an interactive visualization system, supporting intrusion detection over massive network traffic streams. It features a novel time-versus-failed-connections mapping that aids in discovery of attack patterns. The number of failed connections (SYN-SYN/ACK) is a strong indicator of suspicious network flows. IDGraphs offers several flow aggregation methods that help reveal different attack patterns. The system also offers high visual scalability through the use of Histographs. The IDGraphs intrusion detection system detects and analyzes a variety of attacks and anomalies, including port scanning, worm outbreaks, stealthy TCP SYN flooding, and some distributed attacks. In this article, we demonstrate IDGraphs using a single day of NetFlow network traffic traces collected at edge routers at Northwestern University which has several OC-3 links.     

Rootkit的检测与取证分析

Rootkit是目前难以检测、清除、防范和破坏性强的恶意程序。通过分析Rootkit的内核机制,给出检测Rootkit的基本方法,以应用于Rootkit防范和取证分析。     

Image spam analysis and detection

Image spam is unsolicited bulk email, where the message is embedded in an image. Spammers use such images to evade text-based filters. In this research, we analyze and compare two methods for detecting spam images. First, we consider principal component analysis (PCA), where we determine eigenvectors corresponding to a set of spam images and compute scores by projecting images onto the resulting eigenspace. The second approach focuses on the extraction of a broad set of image features and selection of an optimal subset using support vector machines (SVM). Both of these detection strategies provide high accuracy with low computational complexity. Further, we develop a new spam image dataset that cannot be detected using our PCA or SVM approach. This new dataset should prove valuable for improving image spam detection capabilities.     

Biometric template selection and update: a case study in fingerprints

A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.     

On broadcasted game video analysis: event detection,highlight detection,and highlight forecast

Multimedia Tools and Applications - Efficient access to broadcasted computer game videos is urgently demanded due to the emergence of live streaming platforms. The popularity of game video...     

Integrating faces and fingerprints for personal identification

An automatic personal identification system based solely on fingerprints or faces is often not able to meet the system performance requirements. We have developed a prototype biometrics system which integrates faces and fingerprints. The system overcomes the limitations of face recognition systems as well as fingerprint verification systems. The integrated prototype system operates in the identification mode with an admissible response time. The identity established by the system is more reliable than the identity established by a face recognition system. In addition, the proposed decision fusion scheme enables performance improvement by integrating multiple cues with different confidence measures. Experimental results demonstrate that our system performs very well. It meets the response time as well as the accuracy requirements     

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.     

基于CATBL算法的恶意URL检测

为提高对恶意U RL检测的准确率,提出一种结合注意力机制的卷积神经网络和双向长短时记忆网络并联联合算法模型(CATBL).提取用于表达恶意URL二进制文件内容相似性的纹理图像特征,提取URL信息特征及主机信息特征,将这几种特征进行融合,利用CNN(convolutional neural network)挖掘深层次局部特征,采用Attention机制调整权重和双向LSTM(bidirectional long short-term memory)提取全局特征,用于对网络中的恶意URL进行检测.实验结果表明,使用该算法检测恶意U RL的准确率达到98.8％,与传统检测方式相比,具有明显的提升.