1. Fingerprint Matching Chapter 4.1- 4.3 On-Line Fingerprint Verification Anil Jain, Fellow, IEEE, Lin Hong, and Ruud Bolle, Fellow, IEEE Presented by Chris Miles

2. Fingerprint Matching ● Compare two given fingerprints T,I – Return degree of similarity (0->1) – Binary Yes/No ● T -> template, acquired during enrollment ● I -> Input ● Either input images, or feature vectors (minutiae) extracted from them

3. Issues involved with matching ● “extremely difficult problem” ● Displacement ● Rotation ● Partial Overlap – Not completely in image ● Distortion (Non- Linear) – Stretches when pushed down

4. More issues ● Pressure and Skin condition – Pressure, dryness, disease, sweat, dirt, grease, humidity ● Noise – Dirt on the sensor ● Feature Extraction Errors

5. State of the Art ● Many algorithms match high quality images ● Challenge is in low-quality and partial matches ● 20% of the problems (low quality) at FVC2000 caused 80% of the false non-matches ● Many were correctly identified at FVC2002 though

6. Approaches ● Correlation-based matching – Superimpose images – compare pixels ● Minutiae-based matching – Classical Technique – Most popular – Compare extracted minutiae ● Ridge Feature-based matching – Compare the structures of the ridges – Everything else

7. FVC2002

8. Correlation-based Techniques ● T and I are images ● Sum of squared Differences – SSD(T,I) = ||T-I|| 2 = (T-I) T (T-I) = ||T|| 2 + ||I|| 2 – 2T T I – Difference between pixels ● ||T|| 2 + ||I|| 2 are constant under transformation ● Try to maximize correlation – Minimizes difference – CC(T,I) = T T I – Can't be used because of displacement / rotation

9. Maximizing Correlation ● I (  x,  y,  ) – Transformation of I – Rotation around the origin by  – Translation by x,y ● S(T,I) = max CC(T,I (  x,  y,  ) ) – Try them all – take max

10. Correlation Results

11. Doesn't Work ● Non-Linear distortion is significant and not accounted for ● Skin Condition / Pressure cause brightness / contrast and thickness to vary significantly ● Difference correlation – Check max/min correlation values – Matches show greater range ● Computationally Expensive (try them all)

12. Divide and Conquer ● Identify local regions in the image – Pieces of the whole – Interesting regions ● Match them – Sum correlations to find global correlation – Check difference of transforms for each region ● Consolidation

13. Computation Improvements ● Correlation Theorem – Spatial Correlation = point wise mutation in fourier domain – T  I = F -1 (F *(T) x F(I)) – Resultant image has values = correlation for translating to those points – No rotation – Very large computation improvement

14. Computational Improvements ● Multi resolution hierarchical approaches ● Fourier-Mellin – Gives rotational invariance – Other steps reduce accuracy ● Divide and Fourier – Partition into local regions – Match them against each other – Border overlap – Much faster

15. Optical Fingerprint Matching ● Lenses to derive the Fourier transform of the images ● Joint transform correlator for matching ● Very expensive ● Complex ● Immature

16. Minutiae-based Methods ● Classical Technique ● T, I are feature vectors of minutiae ● Minutiae = (x,y,  ) ● Two minutiae match if – Euclidean distance < r 0 – Difference between angles <   – Tolerance Boxes ● r 0 ●  

17. Alignment ● Displacement ● Rotation ● Scale ● Distortion-tolerant transformations ● More transformations – higher degree of freedom for matcher – More false matches

18. Formulation ● M'' j = map(m' j ) – Map applies a geometrical transformation ● mm(m'' j, m i ) returns 1 if they match ● Matching can be formulated as – maximize   mm(map  x,  y,  )(m' P(i) ), m i ) ● P is an unknown function which pairs the minutiae – Which minutiae in I corresponds to which in T m i=1  x,  y, , P

19. P ● Pairs the minutiae ● If P(i) = j – j is i's mate – Most likely pair ● If P(i) = null – i, from T has no mate in I ● If no i matches a j, that j has no mate ● Each minutiae has a maximum of one mate ● Trivial if alignment is known

20. Point Pattern Matching ● Alignment is rarely known ● Cast as point pattern matching – Angular component is only a small difference ● Techniques – Relaxation – Algebraic and operational research solutions – Tree search (pruning) – Search (Energy Minimization) – Hough Transform

21. Matching

23. Relaxation ● Maintain confidence levels between all possible matchings – How likely we think I a matches T b ● Iteratively – Calculate the consistencies the transformations required for those matches – Adjust the confidence levels of points that agree with other points – Decrease others ● Iterative -> slow

24. Algebraic and Operational Research Solutions ● Use algebraic methods ● Requires very restrictive assumptions – Exactly aligns under affine transformation – N = M ● All minutiae perfectly identified ● Assignment problems ● Bipartite graph matching

25. Tree Search (Pruning) ● Search the tree of possible matches – If A matches B, then C matches F, then D matches K ● More assumptions – M=N – No outliers -> All minutiae must match

26. Search (Energy Minimization) ● Cast as a general search problem ● Search towards the optimal set of matches ● Fitness is a function of consistency ● Can use any general search technique – GA – Simulated Annealing

27. Hough Transform ● Brute force search over possible Pairings / rotations / scalings ● Foreach m i in I – Foreach m j in T ● Foreach  in discretized  's – If  I –  J < threshold ● Foreach scale in discretized scales ● dx,dy = m i – map ,scale (m j ) ● A[dx,dy,theta,scale]++ ● Whichever A is highest is the closest transformation ● Can then find pairings easily

28. Hough Transform

29. Improvements ● Vote on neighboring bins / smooth the bins, to get more robust answers ● Parallelize on custom hardware ● Hierarchical discretization ● Chang et al 1997 – Find the principle pair and a course transformation with respect to it that matches the most points – Calculate pairing – Use the pairing to calculate exact alignment

30. Principle Pair ● Brute force for the segment, 2 pts, which can be best mapped to a corresponding segment in T ● Foreach m i in I – Foreach m j in T ● Reset A ● Foreach m i 2 in I – Foreach m j 2 in T ● ,S = Transformation required to turn m i 1,m i 2 into m j 1,m j 2 ● A[ ,S]++ ● Remember the m i 1and m j 1 and  S  with the highest A value ● m i 1,m j 1 are the principle pair and  S is the course transformation

31. Segment Matching ● Udupa, Garg, Sharma 2001 ● Looking for matching segments of similar lengths ● Foreach, determine transforms to match them – Try to match remaining minutiae ● Check consistencies of best matches ● Final score is a combination of the number of mated pairs, the fraction of consistent alignments, and the topological correspondence

32. Minutiae match with pre-alignment ● Idea – pre-align T before storage in database – Align each I just once against the global orientation – Reduces computation in identification systems ● Absolute pre-alignment – Orient everything in a common direction – No reliable system to do this ● Difficult to detect core, or even basic orientation ● Relative pre-alignent – Align I to for each T seperately ● No computation savings

33. M82 – Fbi ● Do course absolute pre- alignment – Center the image around the core location – Orient it with ridges to the left/right averaged facing up ● Find principal pairs – Look at minutiae around the center – Find the best matching pair -> Principle – Calculate course transformation, deformation tensor

34. Avoiding Alignment ● Ordinary Person - “You should go to work” ● Philosopher - “Why?” ● Intrinsic coordinate system – Instead of using global coordinate system orient them with respect to the ridge patterns – Minutiae are defined with respect to this – Transformation / rotation does not change their relative location – Problems partitioning the image into the local coordinate systems

35. Ridge Relative Pre-alignment ● Jain, Hong, Bolle 1997 ● Store minutiae along with information about the ridge attached to it – Oriented along minutiae orientation – Normalized to ridge frequency ● Compare with other ridges until you find a good match – Take as principle pair

36. Comparing ridges ● Convert minutiae in T,I to polar coordinates with respect to the reference minutiae – Reference minutiae = the one with the ridge ● Order them into a list ● Check how many insertions / deletions / transformations are necessary to match the lists ● Variant – Match distances and relative angles of sampled points

37. Results