Finger Print Analysis and Matching Daniel Novák

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1 Finger Print Analysis and Matching Daniel Novák 1.11, 2016, Prague Acknowledgments: Chris Miles,Tamer Uz, Andrzej Drygajlo Handbook of Fingerprint Recognition, Chapter III Sections 1-6

2 Outline - Introduction - Morphology imaging processing - Post - processing - Singularity and Core Detection - Matching 2

3 Morphology Image Processing Daniel Novák , Prague Acknowledgments: José Neira Parra

4 Morphology - Morphology deals with form and structure - Mathematical morphology is a tool for extracting image components useful in: representation and description of region shape (e.g. boundaries) pre- or post-processing (filtering, thinning, etc.) - Based on set theory

5 Morphological Image Processing

6 Morphological Image Processing

7 Dilation & Erosion - The value of the output pixel is the maximum value of all the pixels in the input pixel's neighborhood. - Dilation: B is the structuring element in dilation. A B = { x [( Bˆ) x A] A} - The value of the output pixel is the minimum value of all the pixels in the input pixel's neighborhood. - Erosion: A B = { x ( B) A} x

8 Dilation & Erosion

9 Morphological Image Processing

10 Morphological Image Processing

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13 Morphological Image Processing

14 Morphological Image Processing

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17 Opening & Closing In essence, dilation expands an image and erosion shrinks it. Opening: generally smoothes the contour of an image, breaks isthmuses, eliminates protrusions. Closing: smoothes sections of contours, but it generally fuses breaks, holes, gaps, etc. - Opening of A by structuring element B: Closing: A B = ( A B) A B = ( A B) B B

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19 Morphological Image Processing

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21 Morphological Image Processing

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28 Post-processing - Reduces the width of the ridges to one pixel - Skeletons, spikes - Filling holes, removing small breaks, eliminating bridges between ridges etc. - cleanskeleton: removespur, linkbreak, removebridge

29 Thinning enhance2ridgevalley.m imoutput = bwmorph(imcomplement(imreconstruct),'thin', 'Inf'); %thins the reconstructed image

30 Singularity and Core Detection - Poincare Method

31 Singularity and Core Detection - Poincare Method

32 Singularity and Core Detection - Poincare Method If we know the type of the fingerprint beforehand, false singularities can be eliminated by iteratively smoothing the image with the help of the following observation: Arch fingerprints do not contain singularities Left loop, right loop and tented arch fingerprints contain one loop and one delta Whorl fingerprints contain two loops and two deltas

33 Singularity and Core Detection - Methods based on local features Orientation histograms at local level (3x3) Irregularity Fp1 = findsingularitypoint(fp1); d ij = [r ij.cos2θ ij, r ij sin2 θ ij ]

34 Singularity and Core Detection - Partitioning based methods

35 Feature Extraction

36 Feature Extraction Errors

37 Non-linear distortion

38 Intra-variability

39 Fingerprint matching

40 MATCHING: 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

41 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 - Pressure and Skin condition Pressure, dryness, disease, sweat, dirt, grease, humidity - Noise Dirt on the sensor - Feature Extraction Errors - 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

42 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 - I (Δx,Δy,θ) Maximizing Correlation Transformation of I Rotation around the origin by θ Translation by x,y - S(T,I) = max CC(T,I (Δx,Δy,θ) ) Try them all take max

43 Correlation Results

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

45 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 - P is an unknown function which pairs the minutiae Which minutiae in I corresponds to which in T: allign2

46 Ridge feature based matching

47 Gabor filters

48 Local texture analysis

49 Finger code approach MatchGaborFeat

50 Texture based representation

51 Ridge feature map

52 Ridge feature based matching

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