A Numerical Tour of Signal Processing
Filed under: Computational Geometry, Image processing, Signal Processing, Tutorials
An interesting, full of pratical examples, tour of signal processing from Gabriel Peyré.
Introduction
Wavelet Processing
Approximation, Coding and Compression
Noise and Linear Denoising
Wavelet Non-linear Denoising
Variational Denoising
Variational Image Processing
Audio Processing
Higher Dimensional Signal Processing
Computer Graphics
Sparsity and Redundant Representations
Inverse Problems
Compressive Sensing
Numerical Analysis
Mesh Processing
Geodesic Processing
- Fast Marching in 2D
- Fast Marching in 3D
- Farthest Point Sampling
- Image Compression with Geodesic Triangulation
- Anisotropic Fast Marching
- Geodesic Computation on 3D Meshes
- Shape Matching using the Fast Marching
- Geodesic Surface Remeshing (soon available)
- Geodesic Bending Invariants (soon available)
- Heuristically Driven Propagation (soon available)
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Filterbank-Based Fingerprint Matching
With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification. The localization of core point represents the most critical step of the whole process. A good matching requires an accurate positioning, so the small errors must also be avoided. The usage of complex filtering techniques, can greatly improve accuracy. On the other side, for very poor quality input images, a traditional algorithm can fail even using a hierarchical approach with a multiscale filtering.
- Complex filtering output
We have developed a novel, hybrid technique for core point detection. This algorithm, to our knowledge, is not documented in literature and is based on the mutual information that is exchanged between improved procedures. The core point location is more accurately detected by this merging of multiple techniques.
This new algorithm was tested on FVC2004 training fingerprint images. Test results are available on request. Please email us in order to obtain them.
Demo code (protected P-files) available for performance evaluation. (Matlab Image Processing Toolbox is required).
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We recommend to check the secure connection to PayPal, in order to avoid any fraud.
This donation has to be considered an encouragement to improve the code itself. |
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Pro Version
Fingerprint Recognition System 5.3 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 30 EUROS (less than 42 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Fingerprint Recognition System. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Image Processing Toolbox is required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
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