Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood Presented By: Neeraj Sharma M.S. student, Dongseo University, Pusan South Korea

CGIV 19SEP07 Presented Abstract  Paper proposes a novel approach for matching of minutiae point in fingerprint images.  The key concept used in the approach is the fact that the neighborhood properties for each of the minutiae points remains same in fingerprint images.  One of those characteristics is pair wise distance histogram that we have used in this algorithm.

CGIV 19SEP07 Presented 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, higher degree authentication, and restricted entry places fingerprints suggests easy and cheap method.

CGIV 19SEP07 Presented Biometric Modalities

CGIV 19SEP07 Presented Market Capture by different Biometric modalities

CGIV 19SEP07 Presented Previous Work  Fingerprint Identification is quiet 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 are by Park et al.[2005] and Wamelen et al.[2000].  Park et al. from 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.

CGIV 19SEP07 Presented Different Features in a Fingerprint Ridge Ending Enclosure Bifurcation Island Texture Singular points

CGIV 19SEP07 Presented High level description of algorithms in FVC 2004

CGIV 19SEP07 Presented Extraction of minutiae Image skeleton Gray scale image Minutia features Feature Extraction with CUBS-2005 algorithm Developed by SHARAT et al

CGIV 19SEP07 Presented Problem Simulation  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 Input Pattern

CGIV 19SEP07 Presented Minutiae matching-Aligning two point sets Input Template

CGIV 19SEP07 Presented Process Steps  The algorithm runs in two main steps:- (i) Local matching (ii) Global matching In local matching three stepwise calculations are there: 1.Calculate “k” nearest neighbors for each and every point in both patterns. 2.Calculate histogram of pair wise distances in the neighborhood of every point. 3.Find out the average histogram difference between all the possible cases. 4.Set the threshold level of average histogram difference 5.Compare the average histogram differences with the threshold level.

CGIV 19SEP07 Presented Flow Chart No Point pattern “P” stored in database pattern “Q” is taken that is to be matched with “P” Select a local point and it’s “k” nearest neighbors in patterns P Select a local point and it’s “k” nearest neighbors in patterns Q Make pair wise distance Histogram Average histogram difference < Threshold level Calculate and store transformation parameter Iteration algorithm to calculate final Transformation Parameter Start All point’s in pattern p is examined End

CGIV 19SEP07 Presented 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.(2000)

CGIV 19SEP07 Presented 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 P1 P1n1 P1n2 P1n3 P1n4 P1n5 P1n1 P1n3 P1n4 P1n5 P1n2 P1n3 P1n4 P1n5 P1n3 P1n4 P1n5 P1n4P1n5

CGIV 19SEP07 Presented 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. H 1 =[ ] H 2 =[ ] H 1 - H 2 =[ ] Average histogram diff.(ΔH avg )=(1/10)*Σ(| H 1 - H 2 | i )  Setting of threshold depends on the size of point pattern. Larger the number of points, smaller the threshold count.

CGIV 19SEP07 Presented Transformation Parameter calculation  On the basis of histogram differences, we can make decision on their local matching. Then the transformation parameter is calculated in the following way. Here “r” represents the corresponding Transformation Parameter.

CGIV 19SEP07 Presented 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.

CGIV 19SEP07 Presented Iteration Algorithm  Iteration method is used to converge the results towards dense part of the graph.  For applying this method we need to calculate mean and standard deviation of the distribution.  In graph all the transformation parameters are present, calculated after local matching step.  The mean for this distribution is presented by the “triangle” in centre.

CGIV 19SEP07 Presented Result after first iteration  In the graph, black star is describing the mean for the distribution.  After one iteration step some of the false transformation due to false local match has removed.

CGIV 19SEP07 Presented Result after second iteration  After second iteration, graph converges more towards the dense area.  Black star is the mean point for this distribution shown here.

CGIV 19SEP07 Presented 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”

CGIV 19SEP07 Presented Verification by transforming the template with calculated parameters Template Pattern in Database Transformed version with Parameter ”r”Verification by Overlapping Transformed With “r”

CGIV 19SEP07 Presented Experimental Results (With random generated data) s. no.Total no of point taken No of missing points No. of matched points when no error added No. of matched points when 2% error added No. of matched points when 3% error added

CGIV 19SEP07 Presented Results contd… s. no.Total no of point taken No of missing points No. of matched points when no error added No. of matched points when 2% error added No. of matched points when 3% error added

CGIV 19SEP07 Presented Results with real and random fingerprints  The algorithm was tested on both randomly generated point pattern and real data base.  The results shows the correct identification in more than 98.87% cases with random data.  For real fingerprints we tested the method on some FVC 2004 samples. In most of cases performance was found satisfactory.

CGIV 19SEP07 Presented Limitations  This algorithm is very much dependent on accuracy of feature extraction method used for minutiae determination.  If feature extraction is not done correct then, results can have errors or false acceptances. Which effects performances and accuracy a lot.

CGIV 19SEP07 Presented Future work  This method is still dependent to some other algorithm for feature extraction.  First task is to develop a method for feature extraction from fingerprints.  Other thing to enhance is, its performance mainly on real fingerprint data.

CGIV 19SEP07 Presented 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 others, as histogram is a basis to select the local matching. While in other Random algorithms a lot of calculation is done for local match coz lacking in any basic attribute.  Algorithm doesn’t need very complex system to be implemented for practical/commercial purpose. So compatible to most of systems in use already.

CGIV 19SEP07 Presented Shortcomings  Dependency on an efficient feature extraction scheme.  Histogram differences should be calculated in a better way (Still to think over that…).

CGIV 19SEP07 Presented Thanks for your kind attention.