Fingerprint Recognition Checkpoint Slides

1. Fingerprint RecognitionCheckpoint Slides Andrew Ackerman Professor Ostrovsky

2. Current State • Already Done • Research Fingerprint Acquiring • Research Fingerprint Analyzing • Research Fingerprint Recognition Algorithms • Including Minutiae detection and learned based templates • Current State • Reduced Fingerprint Recognition to Another Problem • Solving for algorithm for problem

3. Fingerprint Recognition • Taking two fingerprint images and seeing if they come from the same finger • Assumption: no two fingers in the world yield the same fingerprint and that fingerprints do not change in time • Much research has been done to validate these assumptions • Complex • Due to a multitude of conditions the same fingerprint scanned twice can look very different • How finger is oriented on scanner • Condition of finger (wet, dry, scarred, etc) • No completely accurate method exists • Many current recognition techniques can tell with a degree of certainty if two fingerprint images match (i.e. come from same finger)

4. Our Method • First try • View the fingerprint edges as a graph structure. • Bifurcation and ending points would become nodes and the edges would be edges • Abandoned due to complexity involved in matching sub graphs of two different graphs • Currently Explored Method • Reduce fingerprint recognition into a problem of topographical equivalence

5. Reduced Problem • Topological Equivalence • Ridges of the fingerprint are just lines that when leave the frame of view go off to infinity • If two fingerprints can be shown to be nearly topologically equivalent then they are most likely the same fingerprint

6. Topological Equivalence Two images are topologically equivalent if you can somehow deform the lines (without crossing them over) to go from one image to the other Not Topologically Equivalent Topologically Equivalent

7. Current Goal • Come up with an algorithm that can determine topological equivalence for relatively simple cases, then generalize to reach most of the cases required for the fingerprint recognition. • Mainly dealing with shapes that include bifurcations and edges and possibly loops • Write software that implements the algorithm • Will use NIST Special Database 4 (fingerprint database) to test algorithm

8. Self Assessment I feel that I have not done all the work I could possibly have accomplished in the fall quarter. However, as I noted at the beginning to Professor Sahai, I had a heavy course load with courses such as CS152B and graduate school applications. With these out of the way for next quarter, I will have much more time to dedicate to coding for CS194. That being said, both Professor Ostrovsky and I feel we are on track to receive good results by the end of the spring quarter. The best case scenario being that our algorithm performs faster and/or more accurately then current algorithms used.

9. Self Assessment Our main project focus has changed. The cryptology part of the project has been dropped for now and the main focus is on fingerprint recognition. I feel a good amount of work has been done on understanding some of the current algorithms for fingerprint recognition. And our current focus is to develop and code our own algorithm to test them. Overall, I feel I have done adequate amount of work for CS 194 during the fall quarter. And I plan to get a good amount of work done over winter break regarding our algorithm and coding it.