CORE POINT DETECTION USING FINE ORIENTATION FIELD

ESTIMATION

M. Usman Akram, Rabia Arshad, Rabia Anwar, Shoab A. Khan

Department of Computer Engineering, EME College, NUST, Rawalpindi, Pakistan

Sarwat Nasir

Department of Telecommunication, MCS, NUST, Rawalpindi, Pakistan

Keywords:

Segmentation, Orientation Field, Poincare Index,Gradient, Optimal Core Point.

Abstract:

Performance of Automatic Fingerprint Identiﬁcation System( AFIS) is greatly inﬂuenced by the detection of

core point. Extraction of best Region Of Interest(ROI) from image can play a vital role for core point detection.

In this paper, we present an improved technique for ﬁne orientation ﬁeld estimation and core point detection.

The distinct feature of our technique is that it gives high detection percentage of core point even in case of low

quality ﬁngerprint images. The proposed algorithm is applied on FVC2004 database. Results of experiments

demonstrate improved performance for detecting core point.

1 INTRODUCTION

Fingerprints have been in use for biometric recogni-

tion since long because of their high acceptability, im-

mutability and individuality.

The probability that two ﬁngerprints are alike is

1 in 1.9 x 10

(W. F. Leung and Luk, 1991). These

features make the use of ﬁngerprints extremely effec-

tive in areas where the provision of a high degree of

security is an issue.

Most Automatic Fingerprint Identiﬁcation sys-

tems (AFIS) are based on local ridge features;

ridge ending and ridge bifurcation, known as

minutiae(A.K Jain and Boole, 1997). Core points

and delta points are critical points in ﬁngerprint. Core

points are the points where the innermost ridge loops

are at their steepest and delta points are points from

which three patterns deviate (Anil Jain, 1998),(Zhang,

2000). Figure 1 shows the location of core point and

delta point in a ﬁngerprint image.

In AFIS, core point plays an important role

(D. Maltoni and Prabhakar, 2003) and it is

widely used for ﬁngerprint matching (Anil Jain,

1998),(D. Maltoni and Prabhakar, 2003),(Maio and

Maltoni, 1997) and classiﬁcation (Wang and Wang.,

2004),(A. K. Jain and Hong, 1999),(Sen Wang, 2002).

The problem with applications related with ﬁnger-

print is how to ﬁx the ﬁngerprint with the help of

Figure 1: Core point and Delta point for a Fingerprint Im-

age.

a reference point so that it would be invariant to er-

ror generated by scanning process(Maio and Maltoni,

1997). This problem can be overcome by detect-

ing core point accurately. Minutiae based ﬁngerprint

matching is widely used in AFIS (Maio and Maltoni,

1997),(Kalle Karu, 1996) where minutiae in neigh-

bor of core point also plays an important role in fre-

quency characteristic ﬁngerprint matching (Maio and

Maltoni, 1997).

A number of algorithms have been proposed for

optimal core point detection and most of them are

based on ridge orientation estimation techniques. A

common method used for core point detection is

Poincare index in which point in the ridge orienta-

tion ﬁeld is classiﬁed as singular point if orientation

along a small closed curve around that point changes

351

Usman Akram M., Arshaf R., Anwar R., Khan S. and Nasir S. (2008).

CORE POINT DETECTION USING FINE ORIENTATION FIELD ESTIMATION.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 351-356

DOI: 10.5220/0001089503510356

 SciTePress

0,±180 or ±360 degrees(Kalle Karu, 1996). A.K.

Jain, S. Prabhakar, L. Hong and S. Pankanti had used

geometry of region technique in (Ani1 K. Jain and

Pankanti, 2000) for reference point detection. Chul-

Hyun Park, Joon-Jae Lee, Mark J.T. Smith and Kil-

Houm Park had proposed a method for reference point

detection especially for arch-type ﬁngerprint.

This paper is organized in six sections. Section

2 deals with the preprocessing of ﬁngerprint image

before locating the core point. Section 3 presents

Poincare index, Geometry of Region Technique and

Direction of Curvature technique while section 4 con-

tains the proposed technique and its algorithm. Com-

parative analysis of experimental results of our tech-

nique with other techniques are presented and dis-

cussed in section 5 followed by conclusion in section

2 FRONT-END PROCESSING

Core point detection process is greatly effected by

quality of ﬁngerprint image. Good image segmenta-

tion and orientation ﬁeld estimation is required for op-

timal core point detection (Maio and Maltoni, 1997).

Figure 2 shows the sequence of steps required for op-

timal core point detection.

Figure 2: Sequential front-end processing for core point de-

tection.

2.1 Image Segmentation

Segmentation is done to extract ﬁngerprint image

from background. In AFIS, the surrounding back-

ground in ﬁngerprint image does not carry any infor-

mation and therefore add to the processing time of all

stages if included. The cutting and cropping out re-

gion containing ﬁngerprint feature, commonly called

Region of Interest(ROI), from the ﬁngerprint image

minimizes the computational time.

Steps for Mean and Variance Based ﬁngerprint

image segmentation technique (Maio and Maltoni,

1997) are summarized as follows:

1. Divide the input image I(i, j) into non-

overlapping blocks with size w×w.

2. Compute the mean value M(I) for each block us-

ing equation 1.

M(I) =

w/2

∑

i=−w/2

w/2

∑

j=−w/2

I(i, j) (1)

3. Use the mean value computed in step 2 to com-

pute the standard deviation value std(I) with

equation 2

std(I) =

w/2

∑

i=−w/2

w/2

∑

j=−w/2

(I(i, j) − M(I))

(2)

4. Select a threshold value empirically. If the std(I)

is greater than threshold value, the block is con-

sidered as foreground otherwise it belongs to

background.

2.2 Image Normalization

Normalization is performed to remove the effect of

sensor noise and gray level background which are

the consequence of difference in ﬁnger pressure ap-

plied at the scanner (Kawagoe and A.Tojo, 1984). Let

I(i, j) denotes the gray-level value at pixel (i,j). The

normalized value N(i, j) for pixel (i, j)is deﬁned in

equation 3 (Maio and Maltoni, 1997)

N(i, j) =











(I(i, j))−M

)

if I(i, j) > M

−

(I(i, j))−M

)

otherwise

(3)

Here M

and V

are the desired mean and variance

respectively. The mean M(I) and variance V (I) of

a gray-level ﬁngerprint image with the dimension

of M × N pixels, are deﬁned using equation 4 and 5

respectively (Maio and Maltoni, 1997).

M(I) =

M−1

∑

i=0

N−1

∑

j=0

I(i, j) (4)

V (I) =

M−1

∑

i=0

N−1

∑

j=0

(I(i, j) − M(I))

(5)

Where I(i, j ) represents the intensity of the pixel

at ith row and jth column. The basic objective

of normalization operation is to reduce the varia-

tions of gray-level values along the ridges and valleys

(A.K Jain and Boole, 1997).

2.3 Orientation Field Estimation

Orientation or direction ﬁeld estimation is not only

used in core point detection but also in ﬁngerprint

matching (A.K Jain and Boole, 1997). The smoothed

orientation ﬁeld based on least mean square algorithm

(A.K Jain and Boole, 1997)(Maio and Maltoni, 1997)

is summarized as follows:

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

352

1. Divide the input image I(i, j) into non-

overlapping blocks with size w x w.

2. Compute the gradients ∂

(i, j) and ∂

(i, j) at the

center of the block.

3. Estimate the local orientation using the equations

6, 7 and 8 (Maio and Maltoni, 1997).

(i, j) =

i+w/2

∑

u=i−w/2

j+w/2

∑

v= j −w/2

2∂

(u,v)∂

(u,v) (6)

(i, j) =

i+w/2

∑

u=i−w/2

j+w/2

∑

v= j −w/2

∂

(u,v)∂

(u,v) (7)

θ(i, j) =

tan

−1



(i, j)



(8)

Here θ(i, j) is the least square estimate of the lo-

cal ridge orientation at the block centered at pixel

(i, j).

4. The local ridge orientation varies slowly in a lo-

cal neighborhood where no core point appears.

The discontinuity in ridge and valley due to noise

can be reduced by applying a low pass ﬁlter.

However, to apply a low pass ﬁlter the orienta-

tion image must ﬁrst be converted to a Contin-

uous Vector Field (CFV). The continuous vector

ﬁeld is deﬁned by the x-component Φ

and the y-

component Φ

computed using equation 9 and 10

respectively (Maio and Maltoni, 1997).

(i, j) = cos(2θ(i, j)) (9)

(i, j) = sin(2θ(i, j)) (10)

5. The two dimensional w

low-pass ﬁlter G

with unit integral is applied to the resultant CFV.

The ﬁltered x-component and y-component of the

CFV are obtained by equations 11 and 12 respec-

tively (Maio and Maltoni, 1997).

(i, j) =

∑

u=−w

∑

v=−w

G(u,v)Φ

(i − uw, j − vw)

(11)

(i, j) =

∑

u=−w

∑

v=−w

G(u,v)Φ

(i − uw, j − vw)

(12)

6. The smoothed orientation ﬁeld at (i, j) is com-

puted by equation 13 (Maio and Maltoni, 1997).

(i, j) =

tan

−1



(i, j)



(13)

Figure 3 shows segmentation, normalization, ori-

entation estimation and core point detection for a ﬁn-

gerprint image.

Figure 3: (a) Original image (b) Segmented image (c) Nor-

malized image (d) Orientation estimation (e) Detected Core

point.

3 CORE POINT DETECTION

TECHNIQUES

The core point is used in both ﬁngerprint classiﬁca-

tion and ﬁngerprint matching using either spatial do-

main (A.K Jain and Boole, 1997)(Ani1 K. Jain and

Pankanti, 2000) or transformed domain (Ani1 K. Jain

and Pankanti, 2000). This section details different

techniques for core point detection.

3.1 Geometry of Region Technique

(GR)

It is very important to ﬁnd the geometry of region to

detect core point as the ridge line curvature varies

sharply near core point region (Maio and Maltoni,

1997).

The GR technique can be summarized as follows.

1. Compute the smoothed orientation ﬁeld θ

(i, j)by

using equation 13 above.

2. Compute ε(i, j) from equation 14 (Maio and Mal-

toni, 1997), which is the sine component of θ

(i, j)

ε(i, j) = sin(θ

(i, j)) (14)

CORE POINT DETECTION USING FINE ORIENTATION FIELD ESTIMATION

353

3. Initialize a label image A which is used to indicate

the core point.

4. Assign the corresponding pixel in the value of

the difference in integrated pixel intensity of each

region A from equation 15 (Maio and Maltoni,

1997).

A(i, j) =

∑

ε(i, j) −

∑

ε(i, j) (15)

The regions R1 and R2 are determined empiri-

cally and also their geometry are designed to cap-

ture the maximum curvature in concave ridges and

should cover at least one ridge.

5. Find pixel (i, j) that have maximum value in A

and assign it as the core point.

6. If the core point still cannot be located, the steps

(1-5) could be iterated for a number of times while

decreasing the window size used in step 1) above.

3.2 Poincare Index

An elegant and practical method based on the

Poincare index was proposed in (Kawagoe and

A.Tojo, 1984). The PC technique can be summarized

as follows (Maio and Maltoni, 1997),(Sen Wang,

2002).

1. Estimate the orientation ﬁeld θ

(i, j) by using

the least square orientation estimation algorithm

given by equation 13 (Maio and Maltoni, 1997)

above.

2. Initialize a label image A which is used to indicate

the core point.

3. For each pixel, compute Poincare index, PC(i, j)

from 16, 17 and 18 (Kawagoe and A.Tojo, 1984)

where

PC(i, j) =

2π

−1

∑

k=0

∆(k) (16)

∆(k) =







δ(k) if δ(k) < π/2

π + δ(k) if δ(k) ≤ −π/2

π − δ(k) otherwise

(17)

and

δ(k) = ε(x

(k+1)

modN

(k+1)

modN

)− ε(x

) (18)

4. The core point should yield the Poincare index be-

tween 0.45-0.51 (Kawagoe and A.Tojo, 1984).

5. The center of the block with the value of one is

considered to be a core point. However if there are

more than one block with that values, the average

calculation is applicable.

3.3 Detection of Curvature Technique

1. Compute the local orientation θ(i, j) by using

equation 8 (Maio and Maltoni, 1997). The input

block size is kxk = 3x3.

2. Smooth the orientation ﬁeld θ

(i, j) by using equa-

tion 13 (Maio and Maltoni, 1997).

3. The difference of direction components is com-

puted for every progressive block from equations

19 and 20.

Di f fY =

∑

k=1

sin2θ(k, 3) −

∑

k=1

sin2θ(k, 1) (19)

Di f f X =

∑

k=1

cos2θ(3,k) −

∑

k=1

cos2θ(1,k) (20)

4. The core point could be located at the correspond-

ing (i, j) where Di f f X and Di f fY are negative.

4 PROPOSED TECHNIQUE

In our proposed method, segmentation and Normal-

ization are done in the same way as described in

section 2 while orientation ﬁeld is estimated by new

method as it greatly effects the core point detection.

4.1 Fine Orientation Field Estimation

The steps for proposed technique are summarized as

follows

1. Divide the input image I(i, j) into non-

overlapping blocks with size w x w. In our

case w = 16.

2. Use 3x3 sobel vertical and horizontal masks from

equations 21 and 22 to compute the gradients

∂

(i, j) and ∂

(i, j) at each pixel (i,j) respectively

which is the center of the block.

sobelHorizontal =





−1 0 1

2 0 2

−1 0 1





(21)

sobelVertical =





−1 −2 −1

0 0 0

1 2 1





(22)

3. Estimate the local orientation using equations 23,

24 and 25 (Lim and S.Jae, 1990).

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

354

(i, j) =

i+w/2

∑

u=i−w/2

j+w/2

∑

v= j −w/2

(∂

(u,v))(∂

(u,v))

(23)

(i, j) =

i+w/2

∑

u=i−w/2

j+w/2

∑

v= j −w/2

∂

(u,v)−∂

(u,v) (24)

(i, j) =

i+w/2

∑

u=i−w/2

j+w/2

∑

v= j−w/2

(∂

(u,v) + ∂

(u,v))

(25)

4. Calculate background certainty and orientation

ﬁeld using equation 26 and 27 respectively

(Zhongchao Shi and Xu, 2004).

coh =

(i, j) +V

(i, j))

∗V

(26)

ifcoh > 10then

θ(i, j) =

tan

−1



(i, j)



(27)

4.2 Optimal Core Point Detection

Steps for our core point detection technique are sum-

marized as follows:

1. Compute the local orientation θ(i, j) by using

equation (27) . The input block size is kxk = 3x3.

2. Locate the region of interest (ROI) based on back-

ground certainty

3. Initialize a label image A which is used to indicate

the core point.

4. Apply steps 3 and 4 on ROI from Poincare Index

technique

5. Find each connected component in A with pixel

values 1. There is normally more than one objects.

Core Point object will always have the largest

area. So we ﬁrst ﬁgure out the object having the

largest area.

6. Then we calculate the centroid of the selected ob-

ject. This centroid gives us the location of core

point.

5 EXPERIMENTAL RESULTS

The performance of our modiﬁcation is tested on

FVC2004 database(FVC, 2004).The database con-

tains 40 different ﬁngers and 8 impressions of each

ﬁnger (40x8=320 ﬁngerprints). The images in

DB1, DB2, DB3 and DB4 are 640×480, 328×364,

300×480 and 288×384 respectively and each hav-

ing a resolution of 500 dpi. For all ﬁngerprint im-

ages core points are detected ideally. Euclidian dis-

tances between ideally detected core points and core

points detected from discussed techniques are calcu-

lated. The core point detection results are compared

and they are summarized in table 1 and table 2.The

decision for accepted location (Accepted Core Point,

ACP) and false location (False Core Point, FCP) is

based on euclidian distances. For all methods maxi-

mum, minimum,mean and standard deviation of error

is calculated. Table 3 shows error performance and

are deﬁned in terms of number of pixels. For above

mentioned techniques ,a comparative analysis of the

computation time, with AMD, 801 MHz, and 1 GB

RAM, is summarized in table 4.

Figure 4: Pictorial comparison of proposed algorithm with

traditional techniques. Ist column shows the original ﬁn-

gerprint images. 2nd and 3rd columns show the results of

Poincare index and Detection of curvature techniques re-

spectively. 4th column show the results of proposed algo-

rithm.

Figure 4 shows the comparison of the proposed

technique with the ones discussed in (Maio and Mal-

toni, 1997),(Maio and Maltoni, 1997),(Kawagoe and

A.Tojo, 1984). Red dot shows the ideal core point

location while the blue dot shows the detected core

point. Figure 5 shows that proposed technique detects

core point correctly even in case of very oily and dry

ﬁngerprint images.

CORE POINT DETECTION USING FINE ORIENTATION FIELD ESTIMATION

355

Table 1: Evaluation Core Point Detection for FVC2004.

ACP ACP FCP FCP

Approaches (Numbers) (%) (Numbers) (%)

Poincare

Index 191 59.68 129 40.32

Detection

of Curvature 263 82.18 57 17.82

Optimal

Core Point 293 91.56 27 8.44

Table 2: Performance Evaluation of Core Point Detection

for Different Quality Images.

Fingerprint Poincare Detection Optimal

Image Index of Curvature Core Point

Quality (%) (%) (%)

Good

Quality 90.3 94.8 98.7

Low

Quality 50.1 63.4 82.3

Rotated

Images 57.8 71.2 87.1

Table 3: Error Performance Evaluation.

Max Min Mean Std

Techniques Error Error Error Deviation

Poincare

Index 240.98 0 25.51 37.75

Detection

of Curvature 240.98 0 22.73 36.01

Optimal

Core Point 240.98 0 14.75 34.39

Table 4: Evaluation of Computational Time.

Processing Time

Techniques Seconds

Poincare Index 0.45

Detection of Curvature 0.25

Optimal Core Point 0.18

Figure 5: 1st row: Oily ﬁngerprint images, 2nd row: Dry

ﬁngerprint images.

6 CONCLUSIONS

Our core point detection technique is useful as it de-

tects the optimal core point with low computation and

it requires simple ﬁeld orientation. Optimal core point

is detected using the ﬁne orientation ﬁeld estimation.

The performance of the proposed technique is better

than the Poincare index and Detection of Curvature

technique. Moreover the proposed technique gives

better results even in case of oily and dry images.

REFERENCES

A. K. Jain, S. P. and Hong, L. (1999). A multichannel ap-

proach to ﬁngerprint classiﬁcation. In IEEE Transac-

tions on PAMI, Vol.21, No.4, pp. 348-359.

A.K Jain, H. L. and Boole, R. (1997). One-line ﬁnger-

print veriﬁcation. In IEEE Trans. PAMI, Vol.19,No.4,

pp.302-314.

Ani1 K. Jain, Salil Prabhakar, L. H. and Pankanti, S.

(2000). Filterbank-based ﬁngerprint matching. In

IEEE Transactions on Image Processing, Vol. 9, No

5, pp. 846-859.

Anil Jain, Ruud Bolle, S. P. (1998). Biometrics-personal

identiﬁcation in networked society. In KluwerAca-

demic Publishers, pp 411.

D. Maltoni, D. Maio, A. K. J. and Prabhakar, S. (2003).

Handbook of ﬁngerprint recognition. In Springer-

Verlag.

FVC (2004). Finger print veriﬁcation contest 2004. In

Available at (http://bias.csr.unibo.it/fvc2004.html).

Kalle Karu, A. J. (1996). Fingerprint classiﬁcation. In Pat-

tern Recognition, vol. 18, No.3, pp.389-404.

Kawagoe, M. and A.Tojo (1984). Fingerprint pattern classi-

ﬁcation. In Pattern Recognition, Vol.17, pp.295-303.

Lim and S.Jae (1990). Two-Dimensional Signal Image Pro-

cessing.

Maio, D. and Maltoni, D. (Jan 1997). Direct gray-scale

minutiae detection in ﬁngerprints. In IEEE Trans. on

Pattern Analysis and Machine Inteligence, vol. 19, pp.

27-40.

Sen Wang, Wei Wei Zhang, Y. S. W. (2002). Fingerprint

classiﬁcation by directional ﬁelds. In Proceedings of

the Fourth IEEE International Conference on Multi-

modal Interfaces (ICMI’02).

W. F. Leung, S. H. Leung, W. H. L. and Luk, A. (1991).

Fingerprint recognition using neural network. In Neu-

ral Networks For Signal Processing - Proceedings Of

The 1991 IEEE Workshop.

Wang, S. and Wang., Y. (2004). Fingerprint enhancement

in the singular point area. In IEEE signal processing

letters, vol. 11, no. 1, pp. 16-19.

Zhang, D. D. (2000). Automated biometrics technologies

and systems. In Kluwer Academic Publishers, pp.331.

Zhongchao Shi, Yangsheng Wang, J. Q. and Xu, K. (2004).

A new segmentation algorithm for low quality ﬁnger-

print image. In IEEE Proceedings of the Third Inter-

national Conference on Image and Graphics.

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

356