Fingerprint Classification by SOM

1. Fingerprint Classification by SOM Wuzhili Vincent 99050056

2. Fingerprint Classification by SOM • Introduction • Detailed Algorithms • Result Analysis • Demo

3. Fingerprint Classification by SOM • Introduction Why goes to this topic: 1. Explore the Industrial Usage of SOM ”Fingerprint Classification Through Self Organizing Feature Maps Modified to Treat Uncertainties” Proceedings of the IEEE, Vol 84, No 10, pp 1497-1512, October 1996 2. Easily start by extending from my Honors Project “Fingerprint Matching”

4. Fingerprint Classification by SOM • Introduction – Topics about Fingerprint Fingerprint Sensor Fingerprint Image Preprocessing Fingerprint Classification Fingerprint Image Compression Fingerprint Matching

5. Fingerprint Manually Classifiedby Experts Right Loop Left Loop Delta Pore Whorl Arch Tented Arch

6. Fingerprint Classification by SOM • Introduction - Automated Classification 1. Might not according to the traditional 5-class scheme 2. Any uniformly distributed categories 3. Consistently and correctly hash new fingerprints into the categories Class1 Class2 Class3 Class4 Class n

7. How to Classify Fingerprints by SOM Introduction - SOM X1 X2 X3 w11 1 w13 w12 w14 3 2 4 2x2 SOM An Input vector X = {x1,x2,x3}

8. How to Classify Fingerprints by SOM For a well-trained SOM: X1 X2 X3 w11 1 w13 Winning Node w12 w14 3 So the Input vector X is class 3 ! 2 4 2x2 SOM An Input vector X = {x1,x2,x3}

9. How to Classify Fingerprints by SOM The Feature Vector of a Fingerprint X: 1. X has dimension 1 x 256: {x1,x2,….x256} 2. It is the directional Map

10. Extract the fingerprint region(right)

11. Extract the Effective Region

12. Locating Fingerprint Core 90% fingerprints centers are located

13. Uncertainty Value: [0, 1] 1. Directions in the good-quality region has good certainty; 2. In the Left figure: Larger certainty ->longer amplitude

14. Training Algorithm1: Original SOM • Contruct a MxM SOM, initialize all the weights • Input a fingerprint vector: {x1,x2,….x256} • Find the winning node dmin where: Dmin = min{||x-w||} • Update the weight vectors: • W(new) =W(old) + Alpha*N*[x-w] • Where N is the neighborhood function corresponding to the SOMnode topology • 5. Repeat 2-4 till Update is not significant

15. Training Algorithm2: Modified SOM • Note: Each fingerprint is associated with a certainty vector C • Contruct a MxM SOM, initialize all weights • Input a fingerprint vector: X{x1,x2,….x256} = C*X + (1-C)*Xavg; • Find the winning node dmin where: Dmin = min{||x-w||} • Update the weight vectors: • W(new) =W(old) + Alpha*N*[x-w] * C • Where N is the neighborhood function corresponding to the SOMnode topology • 5. Repeat 2-4 till Update is not significant

16. Experiment Training Set (DataA) Testing Set (DataB) Fa Fb Fc….. FA FB FC 1. Fa and FA are from the same finger Fb and FB … Fc and FC … 2. Each fingerprint in DataA belongs to a class Class(Fa) = k , k within [1 ~ mxm]

17. Experiment Training Set (DataA) Testing Set (DataB) Fa Fb Fc….. FA FB FC Class(FA) = ClassX ClassX Fa The worst search price to find Fa is Size(ClassX) If all fingerprints are uniformly classified,Less accumulated worst search price-> Less DataA fingerprints are searched when indexing DataB

18. SEARCH% RECOGNIO ON% 3x3 4x4 5x5 8x8 10x10 M 10 12.1 18.8 28.8 91.8 100 S 20 26.0 38.7 54.0 100 O 30 40.1 61.3 80.5 M 40 55.9 86.6 100 50 71.9 100 60 88.5 70 100 80 90 100 10 19.6 16.4 26.4 40.0 62.7 S 20 39.6 38.1 53.1 100 100 O 30 57.3 60.9 82.5 M 40 72.9 84.8 100 50 86.3 100 60 97.5 70 100 80 90 100 Results Search% column : percentage searched in DataA Recognition% Column: percentage found for DataB

19. Advanced work can be done: 1. Increase the layer of SOM to solve the crowded class with many fingerprints 2. Principle Component Analysis to reduce the feature vectorfrom 256 to small dimensions. [40 dimensions are feasible]