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Pair Wise Distance Histogram Based Fingerprint Minutiae Matching Algorithm

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Title: Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood Author: sharma Last modified by: sharma Created Date: 8/11/2007 7:52:51 AM – PowerPoint PPT presentation

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Title: Pair Wise Distance Histogram Based Fingerprint Minutiae Matching Algorithm


1
Pair Wise Distance Histogram Based Fingerprint
Minutiae Matching Algorithm
  • Developed By Neeraj Sharma
  • M.S. student,
  • Dongseo University, Pusan
  • South Korea.

2
Contents
  • Introduction
  • Why Fingerprints some facts
  • Essential Preprocessing (Feature Extraction etc.)
  • Abstract
  • Previous Work
  • Problem Simulation
  • Steps of Algorithm
  • Flow Chart
  • Local Matching
  • Global Matching
  • Results
  • Comparison with Reference method (Wamelen et al)
  • Future work
  • Publications

3
Introduction
  • Fingerprints are most useful biometric feature in
    our body.
  • Due to their durability, stability and uniqueness
    fingerprints are considered the best passwords.
  • In places of access security, high degree
    authentication, and restricted entry,
    fingerprints suggests easy and cheap solutions.

4
Biometric Modalities
5
Market Capture by different Biometric
modalities
6
(No Transcript)
7
Different fingerprints of two fingers
8
Different Features in a Fingerprint
Ridge Ending
9
Feature points extracted
10
Feature Extraction with CUBS-2005 algorithm
(Developed by SHARAT et al)
Minutia features
Extraction of minutiae
Image skeleton
Gray scale image
11
Feature points pattern of same finger
12
High level description of algorithms in FVC
(Fingerprint Verification Competition)2004
13
Abstract
  • Thesis proposes a novel approach for matching of
    minutiae points in fingerprint patterns.
  • The key concept used in the approach is the
    neighborhood properties for each of the minutiae
    points.
  • One of those characteristics is pair wise
    distance histogram, that remains consistent after
    the addition of noise and changes too.

14
Previous Work
  • Fingerprint Identification is quite mature area
    of research. Its almost impossible to describe
    all the previous approaches in a short time here.
  • The previous methods closely related to this
    approach and also taken in reference are by Park
    et al.2005 and Wamelen et al.2004.
  • Park et al. used pair wise distances first ever
    to match fingerprints in their approach before
    two years.
  • Wamelen et al. gave the concept of matching in
    two steps, Local match and Global match.

15
Problem Simulation
Input Pattern
  • The input fingerprint of the same finger seems to
    be different while taken on different times.
  • There may be some translational, rotational or
    scaling changes, depending upon situation.
  • Our aim is to calculate these changes as a
    composite transformation parameter T.
  • The verification is done after transforming the
    input with these parameters, new transformed
    pattern should satisfy desired degree closeness
    with template pattern.

Template
16
Minutiae matching-Aligning two point sets
Template
Input
17
Algorithm Steps
  • The algorithm runs in two main steps-
  • (i) Local matching
  • (ii) Global matching
  • In local matching stepwise calculations are
    there
  • Calculate k nearest neighbors for each and
    every point in both patterns.
  • Calculate histogram of pair wise distances in the
    neighborhood of every point.
  • Find out the average histogram difference between
    all the possible cases.
  • Set the threshold level of average histogram
    difference
  • Compare the average histogram differences with
    the threshold level.

18
Flow Chart
19
Calculation of k Nearest neighbors (Local
match)
  • For the given input fingerprint pattern and the
    template pattern, calculate k nearest neighbors
    in order to distances.
  • Here k is a constant can be calculated with the
    formula given by wamelen et al.(2004)

20
Histogram Calculation (Local Match)
  • Histogram of pair wise distances in their
    neighborhood for each and every point is
    calculated here. It describe the variety of
    distances of particular point in its
    neighborhood.
  • Here for one point P1
  • P1n1, P1n2, P1n3, P1n4, P1n5 are five
    nearest neighbors.
  • Note step size is 0.04unit, here.

P1n2
21
Average Histogram Difference and
Threshold Setting (Local Match)
  • To calculate average histogram differences for
    two points, first subtract the their histograms.
    It comes in a form of matrix. To calculate
    average, just normalize it on corresponding
    scale.
  • H14 2 0 0 2 0 1 2 3 1
  • H21 3 2 5 0 0 2 3 0 1
  • H1 - H2 3 -1 -2 -5 2 0 -1 -1 3 0
  • Average histogram diff.(?H avg)(1/10)S(
    H1 - H2 i)
  • Setting of threshold depends on the size of
    point pattern. Larger the number of points,
    smaller the threshold count.
  • Every matching pair is related with a
    transformation function. That transformation
    parameter is calculated mathematically.

22
Threshold check for a 20 points pattern input
0 1.2 2 2.4 1.2 1.6 0.8 1.6 1.6 0.8 2.8 2 2.8 2.4 2.8 2.4 1.6 2.8 2.8 2.8 1
1.2 0.4 1.2 1.6 0.8 1.2 1.2 1.6 2 1.2 2.8 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 2 2
2 1.2 0.8 1.2 1.6 1.2 2.4 2.4 2.4 1.6 2.4 1.2 1.2 1.2 1.6 1.6 2.8 1.6 0.8 1.6 3
2.4 1.6 0.8 0.4 1.6 0.8 2.4 2 2 1.6 2 0.8 1.2 1.6 1.2 1.6 2 1.2 1.6 1.6 4
1.2 0.8 1.6 2 0.4 1.6 0.8 1.2 1.6 1.2 2.4 1.6 1.6 1.6 1.6 1.6 1.2 1.6 2 2 5
1.6 0.8 0.8 0.8 1.2 0 2.4 1.6 1.6 0.8 2 1.2 1.6 1.6 1.6 1.6 1.6 1.6 2 1.6 6
0.8 1.6 2.4 2.8 1.2 2.4 0 1.6 2 1.6 3.2 2.4 2.4 2.4 2.4 2.4 1.6 2.4 2.8 2.8 7
1.6 1.2 2 2.4 0.8 1.6 1.6 0 0.8 1.6 2 2.4 2.4 2 1.6 1.2 1.6 1.6 2.8 1.6 8
1.6 1.6 2 2.4 1.2 1.6 2 0.8 0 1.6 2 2.4 2.8 2 2.4 2 2 2.4 2.8 2.4 9
0.8 0.8 1.6 1.6 0.8 0.8 1.6 1.6 1.6 0 2 1.2 2 2 2 2 1.6 2 2.4 2 10
2.8 2.4 2.4 2.4 2 2 3.2 2 2 2 0 2.4 2.4 1.6 1.6 1.2 2.4 2 2.8 1.6 11
2 1.6 0.8 1.2 1.2 1.2 2 2 2 1.2 2 0.4 0.8 1.6 1.6 2 1.6 1.2 2 2 12
2.8 1.6 1.2 1.6 1.6 1.6 2.4 2.4 2.8 2 2.4 1.2 0 1.2 1.6 1.6 1.6 1.2 1.6 1.6 13
2 1.2 1.6 2 0.8 1.6 2 1.6 2 1.6 1.6 1.6 1.2 0.4 2 1.2 2 2 1.6 1.6 14
2.8 1.6 1.6 1.6 1.6 1.6 2.4 1.6 2.4 2 1.6 2 1.6 2 0 0.8 2 0.4 2 0.8 15
2.8 1.6 1.2 2 1.6 2 2.4 1.6 2.4 2.4 1.6 2 1.2 1.6 0.8 0.4 2 0.8 2 1.2 16
1.6 1.6 2 2.4 1.2 1.6 1.6 1.6 2 1.6 2.4 2 1.6 2.4 2 2 0 1.6 3.2 2 17
2.4 2 2.4 2.8 1.6 2.4 2.4 1.6 2.4 2 1.6 2 1.2 1.6 2 1.6 1.6 1.6 2.8 1.6 18
2.8 1.6 1.6 1.6 1.6 1.6 2.8 2 2 2.4 2 2 1.6 0.8 1.6 1.2 2.4 1.6 0.8 1.2 19
2.8 1.6 2 2 1.6 1.6 2.8 1.6 2.4 2 1.6 2.4 1.6 1.6 0.8 0.8 2 0.8 2 0 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
23
Transformation Parameter calculation
  • On the basis of histogram differences, we can
    make decision on local matching pairs. Then the
    transformation parameter is calculated in the
    following way by least squire method. Here r
    represents the corresponding Transformation
    Parameter.

24
Axial Representation of all Transformation
Parameters after Local match
  • Three axes represent the translational (in both
    x y direction), rotaional and scale changes.
  • The most dense part in graph represents the
    correct transformation parameters only. We need
    to conclude our results to that part.

25
Mean and Standard Deviation
  • Mean and standard deviation is calculated with
    the following mathematical equations.

26
Global Matching (Iteration Algorithm)
  • Iteration method is used to converge the result
    towards dense part of the graph.
  • For applying this method we need to calculate
    mean and standard deviation of the distribution.
  • In the graph all transformation parameters are
    present, calculated after local matching step.
  • The mean for this distribution is shown by the
    triangle in centre.

27
Result after first iteration
  • In this graph, black triangle is describing the
    mean for the distribution.
  • After one iteration step some of the
    transformation parameters, due to false local
    match got removed.

28
Result after second iteration
  • After second iteration, mean converges more
    towards the dense area.
  • Black triangle is the mean point for this
    distribution shown here.

29
After third iteration
  • Performing iterations to converge the result,
    gives the distribution having least standard
    deviation.
  • Black star in this graph is the desired
    transformation parameter i.e. r

30
Verification by transforming the template with
calculated parameters
Transformed With parameter r
Template Pattern in Database
Transformed version with Parameter r
Verification by Overlapping with original input
pattern
31
Matching Result for Ideal point sets
  • No missing point
  • N60
  • Exactly matching

32
Random and Normalized Noise pattern
(a)
33
Results With Randomly Missing Points
  • Missing points 20
  • Total points60
  • Matching points36
  • Matching factor 0.76
  • Noise factor0.031

34
Matching results after missing points
  • Half no. of points (30) missing from pattern
  • Missing points 30
  • Total points60
  • Matching points16
  • Matching factor 0.91
  • Noise factor0.021

35
Definitions
  • t ? r/(2vn)
  • t is distance of closeness which can be some
    fraction of minimum pair-wise distance
  • ? is called matching factor, depends on point
    pattern
  • rmaximum pair-wise distance/2
  • N is no. of points in the pattern
  • ? is noise factor shows the extent of noise
    added to point pattern
  • ? added average error/mean pair-wise distance

36
Results for different ? and ?
Total points Matching factor(?) Noise factor(?) Time of match(sec) Accuracy()
50 0.508 0.024 1.97 99
60 1.12 0.016 2.53 98
70 4.73 0.019 3.12 94
70 1.23 0.0367 3.10 92
80 3.67 0.0198 4.01 93
80 2.78 0.0276 3.80 90
80 0.8954 0.0431 4.00 89
90 6.01 0.019 5.00 98
90 2.75 0.026 4.84 95
90 2.22 0.0398 4.93 90
90 0.8551 0.049 4.86 83
100 0.6159 0.018 6.063 97
100 2.2081 0.0226 6.00 93
100 3.2826 0.035 5.86 90
100 1.43 0.039 6.00 88
100 2.40 0.0435 6.016 85
37
Performance with missing points regionally
38
Performance with missing points regionally
39
Performance with missing points regionally
40
Performance with a real fingerprint
41
Results with real and random fingerprints
  • The algorithm was tested on both randomly
    generated point pattern and real data base.
  • The results shows correct identification in more
    than 93.73 cases out of 500 tests, with randomly
    generated data.
  • For real fingerprint data, method was tested on
    some FVC (Fingerprint Verification Competition)
    2004 samples. In most of the cases performance
    was found satisfactory.

42
Comparative Performances of two methods over
randomly data
Total points to match Missing Points External noise added () Translation x y Time of match with wamelens method (sec.) Time of match with histogram method (sec.)
30 10 2.4 1.5 2.1 1.36 1.01
40 15 1.85 1.2 1.8 2.07 1.40
50 20 2.1 2.1 2.0 2.90 1.88
60 25 3.1 1.4 1.8 3.96 2.43
70 30 2.3 1.7 1.6 5.01 3.10
80 35 1.9 1.2 1.9 6.30 3.90
90 40 3.2 1.3 1.1 7.72 4.83
100 45 2.9 2.1 1.7 9.26 5.88
110 45 2.6 2.2 1.8 11.10 7.00
120 50 2.5 2.1 1.7 12.95 8.31
43
Comparison of performances
44
Advantages of the Method over others Proposed
earlier
  • This algorithm undergoes two steps, so accuracy
    is good and false acceptance rate is low.
  • Calculation is less complex with comparison to
    other methods proposed yet. Here, histogram is a
    basis to select the local matching pairs, while
    in other randomize algorithms are lacking in any
    basic attribute to compare.
  • Performance is better in case with missing points
    from a specific region.

45
Limitations
  • This algorithm is dependent on accuracy of
    feature extraction method used for minutiae
    extraction.
  • Method performs well if the number of missing
    points in the pattern is less than 50 of total
    minutiae points.

46
Future Work
  • To enhance the performance of algorithm on real
    fingerprint data is also a big challenge.
  • To calculate the computational complexity in big
    O notation.
  • One important task is to develop an independent
    method for feature points extraction from
    fingerprints.

47
Publications
  • Journal
  • Sharma Neeraj, Lee Joon Jae Fingerprint Minutiae
    Matching Algorithm Using Distance Histogram Of
    Neighborhood, Journal of KMMS. (To be published
    in Dec. 2007 edition)
  • International Conferences
  • Sharma Neeraj, Choi Nam Seok, Lee Joon Jae,
    Fingerprint Minutiae Matching Algorithm Using
    Distance Distribution Of Neighborhood, MITA
    (2007), 21-24.
  • Lye Wei Shi, Sharma Neeraj, Choi Nam Seok, Lee
    Joon Jae, Lee Byung Gook, Matching Of Point
    Patterns By Unit Circle, APIS(2007), 263-266.

48
References
  • 1. Wamelen P. B. Van, Li Z., and Iyengar S.
    S. A fast expected time algorithm for the 2-D
    point pattern matching problem. Pattern
    Recognition 37, Elsevier Ltd, (2004), 1699-1711.
  • 2. Park Chul-Hyun, Smith Mark J.T., Boutin
    Mireille, Lee J.J. Fingerprint Matching Using
    the Distribution of the Pairwise Distance Between
    Minutiae, AVBPA (2005), LNCS 3546 (2005),
    693-701.
  • Sakata Koji, Maeda Takuji, Matsushita Masahito,
    Sasakawa Koichi, Tamaki Hishashi Fingerprint
    Authentication based on matching scores with
    other data, ICB, LNCS 3832, (2006), 280-286.
  • Maltoni D., Maio D., Jain A.K., Prabhakar S.
    Handbook of Fingerprint Recognition, Springer
    2003.
  • Chang S.H., Cheng F. H., Hsu Wen-Hsing, Wu
    Guo-Zua Fast algorithm for point pattern
    matching Invariant to translation, rotations and
    scale changes. Pattern Recognition, Elsevier
    Ltd., Vol-30, No.-2, (1997), 311-320.
  • Irani S., Raghavan P. Combinatorial and
    Experimental Result on randomized point matching
    algorithms, Proceeding of the 12th Annual ACM
    symposium on computational geometry,
    Philadelphia, PA, (1996), 68-77.
  • Adjeroh D.A., Nwosu K.C. Multimedia Database
    Management Requirements and Issues, IEEE
    Multimedia. Vol. 4, No. 3, 1997, pp 24-33.

49
  • Thanks for your kind attention.

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