• 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.

•Easy to use A programmer of any skill level should be able to use

•Small File size must be small

•Fast Must not require loads of CPU time

•Cross platform compatible

Must run on any platform with little to no change in code

•Customizable Open source and most changed items must be easily accessible

•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

•Cost Average cost is around $1000.00

•Size Large file size due to unneeded functions

•Resource Requirements

Large amounts of memory and processor time

•Cross compatibility Designed for only certain operating systems, database server, or input devices

•Non-customizable Not open source, making it difficult to customize

•Hard to use Requires large amounts of documentation reading to learn how to use

•Problems associated with commercial SDKs (Software Development Kits):

• C\C++ Compiler

• Basic Text editor or Development IDE

• Hex editor

• Image manipulation program

1. Research Find information on fingerprint matching and image manipulation

2. Design Design and layout library with flow charts

3. Code Code using designs from step 2

4. Compile and Debug

Compile and debug fixing any typographical errors

5. Test Test using sample fingerprints

6. Adjust Adjust for better accuracy

7. Publish Make final copy available

Edge Detection with Logarithm Algorithm

Thinning with Skeletierung’s Algorithm

Breaks found

Final rewritten thin

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

Match Part 2 – Minutia Matching

= Line Branching

= Line Ending

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.

• Image manipulation – including scaling and rotation

• Faster Thinning

• Faster Matching Part1

• Design Embedded System

• Correction of smudged and other imperfections in images

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.