1 / 13

Fingerprints are matching by comparing minutia points Two basic types of minutia points

To solve the problem of limited documentation and example code available on the subject of biometrics. Research that is done in this field can not directly be used in an application; the programmer must develop the code themselves using the research as a guide.

tab
Télécharger la présentation

Fingerprints are matching by comparing minutia points Two basic types of minutia points

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. To solve the problem of limited documentation and example code available on the subject of biometrics. • Research that is done in this field can not directly be used in an application; the programmer must develop the code themselves using the research as a guide.

  2. Problems associated with commercial SDKs (Software Development Kits): • Fingerprints are matching by comparing minutia points • Two basic types of minutia points Line ending Line branching • Fingerprint verification vs fingerprint recognition: • Verification systems need to have more accuracy • Recognition system must be able to process many prints quickly *This project is a verification system

  3. C\C++ Compiler • Basic Text editor or Development IDE • Hex editor • Image manipulation program

  4. Edge Detection with Logarithm Algorithm

  5. Thinning with Skeletierung’s Algorithm Breaks found Final rewritten thin

  6. Match Part 1 – Shifting • Move the verifying print vertically and horizontal to find the spot were the most pixels line up. A true match will have a certain percentage line up. Lines up Does not line up

  7. Match Part 2 – Minutia Matching = Line Branching = Line Ending

  8. My data has shown that this system is not 100% accurate, but no prints that were not suppose to pass did. With a little bit of tuning the accuracy of the system can be easily improved. Also most of the goals for the project have been met, with the exception of speed. As for speed, a revision of Thin() and Match_Part1() are required to optimize these functions. Unfortunately smudged prints still cannot be matched without further correction of the images. Overall the project was a success and continued work will only improve upon it.

  9. Image manipulation – including scaling and rotation • Faster Thinning • Faster Matching Part1 • Design Embedded System • Correction of smudged and other imperfections in images

  10. R. Haralick and L. Shapiro Computer and Robot Vision, Vol 1, Addison-Wesley Publishing Company, 1992. A. Jain and S. Pankanti Automated Fingerprint Indentification and Imageing Systems, Dept. of Comp. Sci. and Eng., Michigan State University, 1996. A. Jain, S. Prabhakar and J. Wang Minutia Verification and Classification for Fingerprint Matching, Dept. Of Comp. Sci. and Eng., Michigan State Unversity. D. Verna Machine Vision, Prentice-Hall, 1991.

More Related