Fingerprint Recognition Professor Ostrovsky Andrew Ackerman

The Idea Including Region information in minutiae matching Including Region information in minutiae matching Reduces amount of matches need Reduces amount of matches need Can help better identify matches Can help better identify matches

The Process Preprocessing image Preprocessing image Getting image into proper input Getting image into proper input 24-bit Bitmap of ridge detail properly enclosed 24-bit Bitmap of ridge detail properly enclosed Thinning Image Thinning Image Zhang-Suen Thinning algorithm Zhang-Suen Thinning algorithm A Fast Parallel Algorithm for Thinning Digital Patterns A Fast Parallel Algorithm for Thinning Digital Patterns Edge Enhancement Edge Enhancement Fill in gaps in edges due to input quality or thinning process Fill in gaps in edges due to input quality or thinning process Code works to extent Code works to extent Region Coloring Region Coloring Colors in regions Colors in regions Spurious Region Removal Spurious Region Removal Remove regions erroneously created in thinning process Remove regions erroneously created in thinning process

Thinning (Zhang-Suen) A(P) = Number of 01 patterns in order set P 2 … P 9 A(P) = Number of 01 patterns in order set P 2 … P 9 B(P) = Number of non-zero neighbors B(P) = Number of non-zero neighbors Algorithm: Algorithm: Do until picture is stable: Do until picture is stable: First Subiteration: Delete P from pattern if: First Subiteration: Delete P from pattern if: A) 2 ≤ B(P) ≤ 6 A) 2 ≤ B(P) ≤ 6 B) A(P) = 1 B) A(P) = 1 C) P 2 * P 4 * P 6 C) P 2 * P 4 * P 6 D) P 4 * P 6 * P 8 D) P 4 * P 6 * P 8 Second Subiteration: Delete P from pattern if: Second Subiteration: Delete P from pattern if: A and B same as above A and B same as above C’) P 2 * P 4 * P 8 C’) P 2 * P 4 * P 8 D’) P 2 * P 6 * P 8 D’) P 2 * P 6 * P 8

Thinning Example Spurious Regions Edge Gaps

Scanning Image Scan image Scan image Move a window down the image Move a window down the image Offset window horizontally Offset window horizontally Offset window vertically Offset window vertically Offset window horizontally Offset window horizontally and vertically

Edge Enhancement Scan the Image Scan the Image For each window: For each window: Find all the endpoints in the window Find all the endpoints in the window For each endpoint pair For each endpoint pair Look at line that goes through the points Look at line that goes through the points If line is “strong” If line is “strong” Draw line between points Draw line between points

Region Coloring Color in Regions Color in Regions Does in one scan of image Does in one scan of image

Spurious Regions Removal Scan the Image Scan the Image For each window For each window If a region is encased in the window and is small enough If a region is encased in the window and is small enough Calculate how often the regions around it border it Calculate how often the regions around it border it Remove the border between the region and the region that least borders it Remove the border between the region and the region that least borders it

Testing Tested 8 different fingerprints of same finger Tested 8 different fingerprints of same finger Choose different minutiae pairs Choose different minutiae pairs Calculated amount of edges crossed between the minutiae Calculated amount of edges crossed between the minutiae Calculated amount of regions crossed between minutiae pair Calculated amount of regions crossed between minutiae pair Compared Results Compared Results

Input Fingerprints

Output of Program

Minutiae Pair 1 Image 1:13 Regions, 20 Edges Image 1:13 Regions, 20 Edges Image 2: 9 Regions, 16 Edges Image 2: 9 Regions, 16 Edges Image 3: 11 Regions, 19 Edges Image 3: 11 Regions, 19 Edges Image 4: 11 Regions, 17 Edges Image 4: 11 Regions, 17 Edges Image 5: 12 Regions, 19 Edges Image 5: 12 Regions, 19 Edges Image 6: 10 Regions, 16 Edges Image 6: 10 Regions, 16 Edges Image 7: 11 Regions, 15 Edges Image 7: 11 Regions, 15 Edges Image 8: 13 Regions, 18 Edges Image 8: 13 Regions, 18 Edges

Minutiae Pair 2 Image 1: 7 Regions, 11 Edges Image 1: 7 Regions, 11 Edges Image 2: 5 Regions, 13 Edges Image 2: 5 Regions, 13 Edges Image 3: 6 Regions, 11 Edges Image 3: 6 Regions, 11 Edges Image 4: 7 Regions, 12 Edges Image 4: 7 Regions, 12 Edges Image 5: 7 Regions, 11 Edges Image 5: 7 Regions, 11 Edges Image 6: 6 Regions, 10 Edges Image 6: 6 Regions, 10 Edges Image 7: 4 Regions, 8 Edges Image 7: 4 Regions, 8 Edges Image 8: 7 Regions, 12 Edges Image 8: 7 Regions, 12 Edges

Statistics Minutiae Pair 1 Minutiae Pair 1 Max Region Difference:4 Max Region Difference:4 Average Region Difference:1.64 Average Region Difference:1.64 Max Edge Difference: 5 Max Edge Difference: 5 Average Edge Difference:2.21 Average Edge Difference:2.21 Minutiae Pair 2 Minutiae Pair 2 Max Region Difference:3 Max Region Difference:3 Average Region Difference:1.25 Average Region Difference:1.25 Max Edge Difference:5 Max Edge Difference:5 Average Edge Difference:1.7 Average Edge Difference:1.7

Future Work Improve Preprocessing Code Improve Preprocessing Code Improve on edge enhancement via algorithms that use edge orientation Improve on edge enhancement via algorithms that use edge orientation Matching Algorithm Matching Algorithm Using processed fingerprints, come up with algorithm that does fingerprint matching Using processed fingerprints, come up with algorithm that does fingerprint matching Use idea of regions and lines between minutiae Use idea of regions and lines between minutiae Fingerprint of Fingerprint Fingerprint of Fingerprint Run X amount of binary tests on a fingerprint and return a vector of size X. This binary vector would identify the finger print Run X amount of binary tests on a fingerprint and return a vector of size X. This binary vector would identify the finger print Matching prints would have similar vectors (hamming distance) Matching prints would have similar vectors (hamming distance) Tests could include information about minutiae as well as regions Tests could include information about minutiae as well as regions