29,567 research outputs found
Fingerprint Identification - New Directions
In most of the algorithms that have been suggested in this report, the fingerprint image is reduced to a relatively short sequence of integers. This reduces the memory size required by the database. Each algorithm is intended to exploit specific properties and features of the fingerprint that vary from finger to finger, and that can be localized relatively fast using digital techniques, thus also reducing the computational time requirements to a minimum. In each case, the sensitivity of the algorithm to small variations in the image was also discussed, with the aim of reducing the False Rejection Rate, and of increasing the general robustness of the algorithm
Multi-aspect, robust, and memory exclusive guest os fingerprinting
Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-Sommelier+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-Sommelier+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-Sommelier+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels
Genome-inspired molecular identification in organic matter via Raman spectroscopy
Rapid, non-destructive characterization of molecular level chemistry for
organic matter (OM) is experimentally challenging. Raman spectroscopy is one of
the most widely used techniques for non-destructive chemical characterization,
although it currently does not provide detailed identification of molecular
components in OM, due to the combination of diffraction-limited spatial
resolution and poor applicability of peak-fitting algorithms. Here, we develop
a genome-inspired collective molecular structure fingerprinting approach, which
utilizes ab initio calculations and data mining techniques to extract molecular
level chemistry from the Raman spectra of OM. We illustrate the power of such
an approach by identifying representative molecular fingerprints in OM, for
which the molecular chemistry is to date inaccessible using non-destructive
characterization techniques. Chemical properties such as aromatic cluster size
distribution and H/C ratio can now be quantified directly using the identified
molecular fingerprints. Our approach will enable non-destructive identification
of chemical signatures with their correlation to the preservation of
biosignatures in OM, accurate detection and quantification of environmental
contamination, as well as objective assessment of OM with respect to their
chemical contents
Feature Level Fusion of Face and Fingerprint Biometrics
The aim of this paper is to study the fusion at feature extraction level for
face and fingerprint biometrics. The proposed approach is based on the fusion
of the two traits by extracting independent feature pointsets from the two
modalities, and making the two pointsets compatible for concatenation.
Moreover, to handle the problem of curse of dimensionality, the feature
pointsets are properly reduced in dimension. Different feature reduction
techniques are implemented, prior and after the feature pointsets fusion, and
the results are duly recorded. The fused feature pointset for the database and
the query face and fingerprint images are matched using techniques based on
either the point pattern matching, or the Delaunay triangulation. Comparative
experiments are conducted on chimeric and real databases, to assess the actual
advantage of the fusion performed at the feature extraction level, in
comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc
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