a multistage detection and elimination of spurious singular points in degraded fingerprints

8/6/2019 A Multistage Detection and Elimination of Spurious Singular Points in Degraded Fingerprints

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(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 9, No.5, May 2011

A Multistage Detection and Elimination of

Spurious Singular Points in Degraded Fingerprints

Zia Saquib, Santosh Kumar Soni, Sweta Suhasaria

Center for Development of Advanced ComputingMumbai, Maharashtra 400049, India

[email protected]

[email protected]

[email protected]

Dimple Parekh, Rekha Vig

NMIMS University,Mumbai, Maharashtra 400056, India

[email protected]

[email protected]

Abstract — Singular point (SP) detection is one of the most

crucial phases in fingerprint authentication systems and is

used for fingerprint classification, alignment and

matching. This paper presents a multistage approach for

detection and elimination of spurious singular points

especially in degraded fingerprints. The approach

comprises three stages. In the first stage, two different

methods, viz., quadrant change and orientation reliabilitymeasure, are independently employed on the same image

to generate two sets of candidate singular points. The

second stage performs the multiscale analysis on a set of

candidate SPs located by reliability method, which

improves the approximation by reducing the list of SPs. In

the third stage, the spurious singular points are detected

and thereby eliminated by taking the intersection of the

two sets of SPs. This model is tested on a proprietary

(Lumidigm Venus V100 OEM Module sensor) fingerprint

database at 500 ppi resolution. The experimental results

show that the approach effectively eliminates the spurious

SPs from the noisy and highly translated/rotated

fingerprint images. The proposed scheme is also comparedwith one of the state-of-the-art techniques, the

experimental results prove its superiority over the later.

Keywords- Spurious Singular Points, Multiscale Analysis,

Orientation Consistency, Quadrant Change, Reliability,

Minimum Inertia, Maximum Inertia.

I. INTRODUCTION

The performance of fingerprint authentication system hascome a long way but it is still influenced by many factors, like:inaccurate detection of singular points (core and delta). Poor-quality and noisy fingerprint images mostly result in false ormissing singular points (SPs), which generally results in

degradation of the overall performance of the authenticationsystems. This paper presents a three-stage approach, whichprimarily focuses on the detection and elimination of spuriousSPs for all types of fingerprint images, especially noisy images.This paper puts forward an effective way to locate a uniquereference point consistently and accurately using tri-methodfusion scheme. Method-A works on the quadrant changeinformation, whereas, Method-B uses pixel-wise reliabilitymeasure of the orientation field followed by multiscale analysisto compute candidate SPs. Intersection of methods A and B

gives the genuine set of SPs. These methods, the proposedscheme and its comparison with one of the state-of-the-arttechniques are explained in detail in section II. Experimentalresults are discussed in sections III, followed by conclusion insection IV.

II. THE PROPOSED SCHEME AND ITS KEY COMPONENTS

A. Quadrant Change: Method-AAs per K. Kryszczuk and A. Drygajlo (2006)[2], a singular point

is the location where the general ridge orientation becomesdiscontinuous. Informally, this can be stated as the area whereridges oriented rightwards change to leftwards and those thatwere oriented upwards turn downwards, and opposite. Thisinformation can be extracted from the quadrant change of theaveraged square gradients. The orthogonal gradientcomponents in the x and y directions are considered separately.In general, each pair of corresponding gradient componentsmanifests the gradient quadrant change by the change of sign.The sign maps PMx and PMy are computed using the Eq. (1):

We need to locate points in whose respective local ridgegradients change sign in both x and y directions. These pointsare obtained by computing the intersection of the two sets of such points for which the sign of the y-directional and x-directional (respectively) gradient component changes, asshown in Eq. (2):

The operator edge in Eq. (2) denotes any edge detector thatworks on binary images, and [ xsp, ysp] are the points where twoquadrants change boundaries intersect, as shown in Figure 1.[ xsp, ysp] are considered as SPs, as shown in Figure 2. Thismethod works well with good quality gray-level images, butthe moment image quality degrades, it starts resulting inspurious SPs and eventually becomes ineffective, as shown inFigure 2.

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Vol. 9, No.5, May 2011

B. Orientation Reliability Measure: Method B

As per Z. Saquib and S. K. Soni (2011)[6], M. Khalil, D.

Muhammad (2010)[5], the raw fingerprint image is first filtered

using Gabor filter. Then, 'reliability' of ridge orientation map

is calculated, followed by the calculation of the area of

moment of inertia about the orientation axis (the min. inertia)

and an axis perpendicular (the max. inertia), as given in Eq.

(3) and (4):

min_inertia(x, y) = (((Gyy + Gxx) - (Gxx - Gyy ) * φ'x) - (Gxy * φ'y))/2

max_inerita(x, y) = Gyy + Gxx – min_inertia(x, y)

where, φ'x and φ'y are cosine and sine of doubled angles (ridge

orientations). The reliability measure is given by Eq. (5):

Reliability Measure = 1.0 – min_inertia/max_inertia

All such pixels with reliability measure below an empiricallydetermined threshold (here, it is 0.035) are considered as thecandidate SPs. The pixels with deep blue shades are thepossible SPs, as shown in Figure 3, and the corresponding SPsare shown in Figure 4, which is inclusive of both genuine andspurious.

C. MultiScale Analysis

As per T. Van and H. Lee (2009)[1], a multiscale analysis

(see Figure 5) of orientation consistency is used to search thelocal minimum orientation consistency from large scale to fine

scale. The orientation consistency-based technique can be

summarized as follows:

1) Compute the orientation consistency Cons(s) of each block based on the outside 8s surrounding blocks of its (2s+1) x(2s+1) neighborhood.

2) Find the minimum orientation consistency denoted as

Consmin (s). Compute candidate threshold as,

3) Select the blocks if their Cons(s) < T.

4) Compute dx(s) and dy(s), and select the blocks with both

dx(s) and dy(s) larger than 0 as the candidate blocks in

the next finer scale:

Figure 1. Horizontal and Vertical maps.

Figure 2. Genuine and Spurious SPs based on

Quadrant Change Information.

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Figure 3. Reliability Image

Figure 4. Genuine and Spurious SPs based on Reliability Measure.

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Vol. 9, No.5, May 2011

5) If no candidate blocks for the reference point are located,

let T = T + 0.01, go to step 3).

6) Repeat steps 1), 2), 3), 4), and 5) in the selected candidateblocks with s = s-1 until s = 1.

7) Locate the block with minimum orientation consistency

Cons(1) from the selected finest scale blocks as the

unique reference point.

We have performed multiscale analysis over the set of SPs

given by reliability measure stage for better approximation of

the genuine SPs, as explained in sub-section D. Multiscale

analysis helps in reducing the list of SPs further by isolating

and removing the false SPs.

D. Proposed Approach: A Multistage Detection and

Elimination of Spurious SPs

The proposed approach, as shown in Figure 6, comprises thestate-of-the-art methods (with some modifications/tuning)presented in sub-sections A, B and C. Firstly, the two sets of candidate SPs are generated using the methods: i) quadrantchange information and ii) reliability measure of the orientationfield. In order to have better approximation, multiscale analysisis performed over the candidate SPs from reliability measure,which reduces (or minimizes) the list by identifying, andthereby ignoring most of such pixels which are not likely to bethe SPs. Finally, the genuine SPs are confirmed by taking the

intersection of the two sets of SPs from the above two methods,which then filters out the false SPs, if any, leaving behindgenuine SPs. These stages are shown together in Figure 6. Theexperimental results are shown in Figure 7 and 8. In Figure 8,first column depicts the raw images, second column shows theresults using Quality Change and Reliability methods, thirdcolumn displays SPs by Quadrant Change Information (blue),Reliability Measure (red), Multiscale Analysis (green) and thefourth column presents results from the proposed scheme(genuine SPs are depicted by orange color). Few improved

cases are also presented in Figure 9, where the raw imageschosen are relatively of much poorer quality than the images inFigure 8.

III. EXPERIMENTAL RESULTS

Proprietary (Lumidigm Venus V100 OEM Module sensor)dataset has been chosen as test data to evaluate the impact of the proposed multistage scheme for detection and elimination

of spurious SPs. The scheme is implemented in MATLAB. Theexperimental results show that this approach satisfactorilyimproves the accuracy of detection of correct singular points innoisy and highly transformed (translated/rotated) fingerprintimages. Only select cases (highly degraded/translated/rotated)have been chosen to measure the effectiveness of the approach.Few of them are presented in Figure 7 and 8. Some improvedcases are also displayed, as shown in Figure 9, where severelydistorted/poorly overlapped fingerprint images are chosen,which present real challenges in the fields.

IV. CONCLUSION

Genuine SPs are very crucial towards attaining highaccuracy and performance of the authentication systems. Thus,

spurious SPs need to be completely removed. In this paper, amultistage scheme is proposed for detection and elimination of spurious singular points, especially in highly degraded,translated and rotated fingerprint images. Experimental resultsclearly show that the three methods in combination effectivelyremove (or minimize) the spurious singular points. The schemeis tested against some select difficult cases. Also, this method(fourth column in Figure 8), upon comparison with theapproach presented by Z. Saquib, S. K. Soni (2011) (secondcolumn in Figure 8), is found better.

ACKNOWLEDGMENT

We wish to extend our sincere thanks to the Department of Information Technology (DIT), Ministry of Communications

and Information Technology, Govt. of India, for assigning us abiometric project: “BharatiyaAFIS”. This work is carried out asa part of the same project.

REFERENCES

[1] T. Van and H. Lee,“An efficient algorithm for fingerprint reference- point detection”, IEEE 2009.

[2] K. Kryszczuk and A. Drygajlo, “Singular point detection in fingerprintsusing quadrant change information”, The 18th International Conferenceon Pattern Recognition (ICPR'06), 2006.

[3] D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. New York: Springer, 2003.

[4] L. Hong, Y. Wan, and A. Jain, “Fingerprint image enhancement:algorithm and performance evaluation”, IEEE Transactions On PatternAnalysis And Machine Intelligence, Vol. 20, No. 8, 1998.

[5] M. Khalil, D. Muhammad, M. Khan, Mohammed, “Singular pointsdetection using fingerprint orientation field reliability”, InternationalJournal of Physical Sciences Vol. 5(4), pp. 352-357, 2010.

[6] Z. Saquib, S. Soni, S. Suhasaria, D. Parekh, R. Vig, “A fault-tolerantapproach for detection of singular points in noisy fingerprint images”,International Journal of Computer Security Issues, Volume 8, 2011.

[7] http://en.wikipedia.org/wiki/Euclidean_distance

[8] Kovesi PD (2008). MATLAB and Octave Functions for ComputerVision and Image Processing, in School of Computer Science andSoftware Engineering, The University of Western Australia. Availablefrom http://www.csse.uwa.edu.au/~pk/research/matlabfns/.

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Figure 5. The multiscale analysis of orientation consistency.




Vol. 9, No.5, May 2011

Figure 6. Proposed Scheme.

Figure 7. SPs before Intersection (left), SPs after Intersection (right).

Spurious SP

Genuine SP Genuine SP




Vol. 9, No.5, May 2011

Fingerprint ImageQuadrant Change &

Reliability methods

Quadrant Change, Reliability &

Multiscale methods

(before Intersection)

Quadrant Change, Reliability

& Multiscale methods

(after Intersection)

001_5_10.bmp

001_5_68.bmp

003_5_73.bmp

006_5_16.bmp




Vol. 9, No.5, May 2011

006_5_34.bmp

006_5_55.bmp

006_5_60.bmp

006_5_75.bmp




Vol. 9, No.5, May 2011

007_5_2.bmp

007_5_23.bmp

007_5_67.bmp

001_5_26.bmp

Figure 8. Experimental Results from Lumidigm Dataset: (first column) Raw Images, (second column) Results using Quality Change and

Reliability methods, (third column) Blue SPs by Quadrant Change Information, Red SPs by Reliability Measure, Green SPs by Multiscale

Analysis and (fourth column) Proposed Scheme – Genuine SPs are depicted by Orange SPs.




Vol. 9, No.5, May 2011

Sr.No. Fingerprint ImageQuadrant Change & Reliability

methods

Quadrant Change, Reliability

& Multiscale methods

(proposed approach)

1.

006_5_65.bmp (There is no SP present in the Raw Image)

2.

006_5_66.bmp (Only Delta should have been marked)

3.

007_5_25.bmp (Only single Core is present)

4.

001_5_15.bmp (Only single Core is present)

Figure 9. Experimental Results from Lumidigm Dataset: Third column represent improved cases, inclusive of both genuine

and spurious SPs (please zoom to view them properly).

a multistage detection and elimination of spurious singular points in degraded fingerprints

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