JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 15, ISSUE 2, OCTOBER 2012

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Fingerprint Core Point Detection using Two-Conditional Filters

G. A. Bahgat, A. H. Khalil, N.S. Abdel Kader and S. Mashali

Abstract — The core point (CP), in the fingerprint image, can be used for the fast alignment between any two fingerprints needed to be

matched. One of the fingerprint types, the plain arch type, is a challenging problem for the detection of this point. This paper presents a fast

CP detection method, based on two conditional filters to detect the CP in all the fingerprint types. A modified CP conditional filter is combined

with another developed filter. Analysis is made on the first filter design and on the effect of adding the second filter. Also, constrains on the

ridge orientation consistency is added to increase the CP detection accuracy. This method is characterized by its simplicity of design that will

make it feasible for the hardware implementation. The performance is tested on the fingerprints taken from the digital sensors (FVC2002

DB2 and FVC2004 DB1 databases) and on inked fingerprints (NIST 4 database), and compared with other CP detection techniques. The

results show that the modified CP detection method is faster, simpler in design while having a good accuracy.

Index Terms — Core point detection, design analysis, filters, fingerprint images, orientation consistency, reference point detection.

—————————— ——————————

1 INTRODUCTION

HE fingerprint recognition systems are used in many ap-plications around us. They are used in accessing the lap-tops, the employee recognition in companies, and the cus-

tomer verification in the banking processes. Some of these ap-plications are implemented using embedded systems, which require using system modules that consume a small area in the system. This can be achieved by using simple designed algo-rithms. The most common digital modules used are FPGA modules ([1],[2]) and DSP modules [3]. Besides, many applica-tions work in real time environment that require fast recogni-tion process. The fingerprint recognition includes alignment procedure, which can be implicit or explicit [4]. The explicit alignment can reduce the matching time per finger by 43% [5]. The fingerprint features are referenced relative to the CP in [6].

The fingerprint consists of line patterns called ridges that tend to enter from one side of the fingerprint and exiting from the opposite side. They may exhibit high curvature pattern [7]. One of these patterns is the loop. It is the innermost recurving ridge. It can exist in two forms (Fig. 1); an upper loop and a lower loop. An upper core point is defined on the peak or on a point inside the upper loop. It is usually located in the central area of the fingerprint [8]. The CP is defined as the upper core point in the loop and the whorl types of the fingerprint [9]. In other types, which do not contain loops, it is defined as the center of the highest curvature region [10]. The forensics defini-tions [11] partially coincide with these definitions except that the CP is defined at the shoulder of the loop. The manual CPs are always defined on a ridge.

The fingerprint images are classified into five classes [7]:

The two classes: the left loop and the right loop. Both contain one core point. The whorl class contains two cores points (Fig. 1). The plain arch class contains no core points (Fig. 2a). It is formed of ridges that tend to rise in the center of the pattern, forming a wave-like pattern. Finally, the tented arch class con-tains high curvature area (Fig. 2b, c, and d). It possesses either: an angle, an up-thrust, or semi-loop [11]. The angular type is formed by the intersection of two ridge endings. In the up-thrust type, there is an ending ridge. The third type of the tented arch is the semi-loop which loses one of the three cha-racteristics of the loop, which are: (a) the existence of sufficient recurve, (b) the existence of delta point shape, and (c) the ridge count, between the core and the delta point, is greater than zero.

Fig. 1. A fingerprint image of type whorl containing an upper and a lower

loop.

© 2012 JCSE

www.Journalcse.co.uk

T

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G.A. Bahgat is with the Department of Computers and Systems, Electronic Research Institute, Giza, Egypt.

A.H. Khalil is with the Department of Electronics and Electrical Commu-nications, Faculty of Engineering, Cairo University, Giza, Egypt.

N.S. Abdel Kader is with the Department of Electronics and Electrical Communications, Faculty of Engineering, Cairo University, Giza, Egypt.

S. Mashali is with the Department of Computers and Systems, Electronic Research Institute, Giza, Egypt.

13

Fig. 2. Arch types: (a) Plain arch. (b) Angular. (c) Up-thrust. (d) Insufficient

loop.

There are methods that can effectively detect the CP in the

loop and the whorl classes of the fingerprints and some tented arch types such as the semi-loop type. CP is detected at the average of the crossing points of the lines normal to the ridges [12]. This method is iterative consuming a large execution time, besides; the CP is detected outside the boundary in case of the plain arch. A multi-resolution method, based on the integration of the sine components in two adjacent regions is used to cap-ture the maximum curvature in concave ridges [9]. It fails when the CP is near to the border. A hierarchical analysis of the orientation coherence detects the CP [13]. Complex filters based method detect the points of symmetry in the complex valued tensor orientation field [14]. The tensor orientation field is a squared function of the orientation field. But their accuracy is low in detecting the CP in the arch type and a second order filter is used.

A recent category of the CP detection methods are presented in ([15], [16], [17]). This category works on the line appearing in the ridge orientation map, presented by a gray-scale with an angle range of (0o≤θ<180o). This line is generated from the dis-continuity between the angle values 0o and 180o. Thus, it is called the discontinuous line (DL). An edge-map based method locates the DL by applying an edge detection method on the ridge orientation map [15, 16]. The CP is extracted by analyz-ing the orientation consistency around the end points of the DL. This method can detect the CP with high speed. It locates the CP at the arch types at the point of minimum orientation consistency located on the DL. Another method locates the CP at the point with the highest curvature value along the DL [17].

In the direction of using a simple design that is more conve-nient for the hardware implementation, a singular point detec-tion method based on applying a masking technique directly on the orientation map is given in [18]. Its execution time is less than that of Poincare Index method [7] by a factor of 0.12, but with almost the same accuracy. A fast technique, based on a conditional filter, detects the CP in the loop, the whorl and some tented arch types, while it defines no point in the plain arch type [19].

This paper increases the accuracy of the CP conditional filter presented in [19], by combining it with a developed DL detec-tion filter and putting constrains on the orientation consistency. An anlysis is presented on the design of the CP filter and on the effect of adding the DL filter. The results show that the modified CP detection method achieves better accuracy with less processing detection time, and using simpler arithmetic modules compared to the edge map based method.

The rest of this paper is organized as follows: the definitions are given in section 2. Section 3 describes the proposed CP de-tection method. Its performance, compared with other me-thods, are measured and discussed in section 4. Finally, the conclusions are presented in section 5.

2 ORIENTATION MAP AND CP CONDITIONAL FILTER

2.1 Smoothed Orientation Map

The CP calculation is based on the ridge orientation, which is defined as the angle θo(x, y) made by the ridges, crossing through a small neighborhood centered at a point (x, y), with the horizontal axis [7]. The orientation is calculated at discrete positions of a step w; where w is slightly greater than the ridge width. The image can be divided into blocks of size w x w. Then, a special averaging technique is used to calculate the orientation θo(i, j) of each block (i, j). Direct orientation averag-ing is not applicable here due to the discontinuity at 0o and 180o [7]. The conventional method, for the orientation map cal-culation, is the gradient-based method [7]. Its equations are given by

,, (1)

, (2)

,, (3)

(3) , (4)

where is the gradient, x and y are the pixel coordinates of the center of the block (i, j) and b=(w/2 – 1). The gradient compo-nents can be calculated using the simple gradient operator: the Sobel mask [20]. It is characterized by its effectiveness in aver-aging, since it gives more weight to the center pixel

Adaptive smoothing technique [13] based on the orientation consistency is used to increase the accuracy of the orientation map, and, consequently, increase the accuracy of the CP detec-tion. The orientation consistency describes how well the ridge orientations, in a neighborhood, are consistent with the domi-nant orientation in the neighborhood. The technique smoothes the orientation map and, at the same time, does not affect the accuracy of the CP location; the adaptive window is used to attenuate the noise of the orientation field while maintaining the detailed orientation information in the high curvature re-gions. The smoothed orientation map is obtained by [13]

, (5)

where θs(i, j) is the smoothed orientation and Ω(s) is the sur-rounding neighborhood of the block (i, j), which is defined by the (2s+1) x (2s+1) outside surrounding blocks, and s is the con-sistency level. The orientation consistency equation is given by

, (6)

where M is the number of the surrounding blocks. The ridge orientation maps are shown, by a gray scale presentation, in Fig. 3. The axis of the orientation values is the standard axis;

Ω(s)l)(k,o

Ω(s)l)(k,o

sl)(k,(

l)(k,(

=j)(i,θ)2θcos

)2θsin

tan2

1 1-

(a) (d) (c) (b)

yyxx

xyo

oGG

G+=j)(i,θ

2.tan

2

190 1

b

b=h

b

b=k

yyy k+yh,+x=yx,G2

2

b

b=h

b

b=k

xxx k+yh,+x=yx,G

k+yh,+xk+yh,+x=yx,G y

b

b=h

b

b=k

xxy

.

M

j))(i,(+j))(i,(

=s)jcons(iΩ(s)j)(i,Ω(s)j)(i,

22

2θsin2θcos

,,

14

the x-axis points to the right and the y-axis points upwards. The orientation values of the fingerprint images of Fig. 3 are also displayed by short lines in Fig. 4.

2.2 CP Conditional Filters

The n x n CP conditional filter [19] operation is shown in Fig. 5a. it operates on the surrounding blocks arranged in an anti-clockwise direction from B1 to B4(n-1). It is applied on each seg-mented block B(i, j) in the smoothed orientation map. The orientation values (θ1.. θ4(n-1)) of the blocks (B1 to B4(n-1) ) are checked if it is in the determined range or not, according to the following equation

, (7)

where k ={1..4(n-1)}, is the index of the block in the filter, Ck is the output of the conditional operation, Lk is the lowest allowed orientation value for the block Bk, and Hk is a value, below which the orientation value for the block is allowed. The num-ber of the filter blocks that satisfy the required range is accu-mulated by

, (8)

where A(i, j) is the accumulation of the conditional filter for the block B(i, j).

Fig. 3. The orientation maps of the non arch types: (a) left loop, and (b) whorl, and

the orientation map of the arch types: (c) up-thrust tented arch, and (d) plain arch.

Fig. 4. The orientation values are presented by short lines on the equivalent

fingerprint images shown in Fig. 3: the non arch types: (a) left loop, and (b) whorl,

and the arch types: (c) up-thrust tented arch, and (d) plain arch.

Fig. 5. The conditional filters operation (a) The CP filter. (b) The DL filter.

3 PROPOSED CP DETECTION METHOD

In this section, the proposed DL conditional filter is presented followed by the combined conditional filters based method. Then, the whole procedure of the CP detection is given, with the use of both CP and DL conditional filters.

3.1 Developed DL Conditional Filter

The disontinious line (DL) in the orientation map is defined as a line generated in the orientation map presented by a gray-scale due to the discontinuity between the orientation values; 0o and 180o. The presence of DL depends on the presence of the curved ridges with the horizontal tangents. The DL can be pre-sented by

, (9)

where Δ is a small value. The DL conditional filter operates on two adjacent blocks (Fig. 5b). 3.2 Proposed Combined CP Detection Method

The CP conditional filter operation [19] is modified and combined with the DL conditional filter. The operation of the proposed method is as follows:

1. For each segmented smoothed orientation θs(i, j), the CP conditional filter centered at the block B(i, j) is applied ((7) and (8)). Set a=4*(n-1).

2. If there exists blocks with accumulation A(i, j)=a, then the CP location is determined by

, (10)

, (11) and the procedure ends. Otherwise, a is reduced by one.

3. If a>D1, then go to step 2. Otherwise, continue. D1 is a threshold value that separates the non arch types from the arch types.

4. If there exists two adjacent blocks B(i, j) and B(i, j+1) that satisfies (9), and if a=D1 , then the CP location is deter-mined by

o

o

o

o

o

o

ji

jijiDL

1801,)180(

&)0(,0:),(

otherwise

HLifCB kkk

kk

0

1:

)1(4

1

),(n

k

kCjiA

wj)(i,CP=y)(x,CP blocklocation

ajiAjiconsCPji

block ),(:),(minarg,

B1

j)B(i, Bn

B4(n-1)

(a)

B(i, j+1) B(i , j)

(b)

(a) (b) (c) (d)

(a) (b) (c) (d)

15

, (12) and (11), then the procedure ends. Otherwise, continue.

5. a is reduced by one, then the CP location is determined by (12) and (11), then the procedure ends.

3.3 The CP Detection System

The image is divided into blocks of size w x w. Then, the seg-mentation is applied on each block. The segmentation is de-fined as the separation between the fingerprint areas (fore-ground) from the image background. It is applied on the orien-tation map to prevent the false detection of the CP. The mean of each block is calculated relative to the global mean of the image and the variance of each block is calculated relative to the difference between the global, maximum and minimum, variance value [21]. The block is segmented if the relative mean is less than an upper limit (mth), and the relative variance is smaller than a lower limit (vth). Morphological operations are applied that include dilation and erosion to fill the holes in the foreground and isolate the points in the background [22]. The structuring element size is (str).

The gradient-based method [7] is applied on each block ((1)-(4)) with averaging window size w x w. An adaptive smoothing technique [13], using (5) and (6), is used to smooth the orienta-tion map. Then, the proposed combined conditional filters scan the segmented orientation map.

4 EXPERIMENTAL RESULTS AND DISCUSSION

The FVC2002 DB2 [23], FVC2004 DB1 [24] and NIST 4 [25] da-tabases are used to test the performance of the CP detection methods. The fingerprints in FVC2002 DB2 database are taken by a capacitive sensor. The images size is 256 x 364 pixels. The fingerprints in FVC2004 DB1 database is taken by an optical sensor. The images size is 640 x 480 pixels. Both databases im-ages are of resolution 500 dpi. Also, they contain 8 impressions for each finger that are taken in different skin conditions; nor-mal, dry and wet conditions. The NIST 4 database: the finger-print images are two rollings of the same finger. Image size is 512 x 512 pixels with resolution 19.7 pixels/mm. The CP detec-tion training data set is taken from FVC2002 DB1 set (B), five arch fingerpers from FVC2004 DB2 set (A) and a sample from NIST 4.

The fingerprint images is divided into blocks of size w=5 pixels [13] for the FVC images and w=10 for the NIST 4 images. The segmentation parameters values used with the FVC data-base are: mth =25, vth = 0.05 and str = 5 pixels. The parameters values used with the NSIT 4 database are: mth = 45, vth = 0.06 and str = 5 pixels. The CP filter size is taken 5 x 5 blocks. The orientation values limit Lk and Hk, are given in degrees as fol-lows: R1 = {[0, 50] U [120, 180]}, R2 = [0,80], R3 = [15, 90], R4 = [15, 95], R5 = [15,100], R6 = [20, 120], R7 = [20, 115], R8 = [25, 125], R9 = [40,140], R10 = [40, 150], R11 = [40,160], R12 = [40,165], R13 = [60,175], R14 = [100,180], R15 = [122,180] and R16 = {[120,180] U [0, 40]}; where Rk=[Lk Hk].

The accuracy of the CP detection methods are measured rel-ative to the CP detected manually. CP is defined for each fin-gerprint unless the fingerprint scanned is shifted and its center is not shown. CP detection rate is used as a measure; it is the ratio of the number of the CPs detected correctly by the algo-rithm, to the number of the CPs detected manually. The loca-tion of the detected CPs is compared with the manual in-

spected CPs using the Euclidian distance ([13], [10]). If the dis-tance is between 10 and 20 pixels, it is considered as a small error that can be caused by both the human vision and the al-gorithm. If the distance is between 20 and 40 pixels, it is consi-dered as a significant error, which may affect the subsequent processing steps. If the distance is larger than 40 pixels, the CP is considered a false detected point [10].

In order to demonstrate the performance of the proposed CP detection method, it is compared with: the fast edge-map based method [15], which is the same category of the proposed method. The edge map-based method is implemented with the same preprocessing of the proposed method. The latency of detection is taken by a length of less than 40 pixels [10]. The testing images are: 800 images in the FVC2002 DB2 set(A), the arch images (120 images) in the FVC2004 set(A), and 100 im-ages in NIST4.

The accuracy is as shown in Table 1 for different designs of D1 values of the CP filter, with and without the DL conditional filter. Also, the consistency constrains are applied for the CONSH case (Table 1). The constrain values on the consistency (cons) are given in Table 2. The accuracy results for the FVC2002 DB2 set (A) (Table 1) show that the CP filter performs best for the CP detection without the DL filter, since there are only 3 plain arches in this database. In the FVC2004 DB1 set (A) database, the best results are shown when the CP and DL fil-ters are used with consistency conditions, because all the tested images are of the arch type. As for the NIST 4 database, the best results are given for the CP detection with the DL filter and consistyency constrains. The sample tested from NIST4 contains 40% arch images. The optimal design is that depends on the combined filters with orientation sonsistency constrains.

Moreover, the average execution time of the CP detection methods per fingerprint is shown in Table 3. It is reduced by the proposed method by an average factor of 3.2 compared to the edge map based method. This can be explained by analyz-ing the minimum arithmetic operations used by both methods (Table 4). The edge map based method uses complicated opera-tions that consumes more time in its processing. The average computational complexity of the proposed method is of order O(m), where m is the number of blocks in the orientation map. The main error cause is generated by the distorted areas in the fingerprint images that cause an error in the equivalent areas in the orientation map.

TABLE 1

THE ACCURACY OF THE CP DETECTION METHODS (IN PERCENTAGE).

(NA:NOT AVAILABLE)

method FVC2002 DB2

set (A)

800 images

FVC2004 DB1

120 Arch

images

NIST4

100

images

Edge map [15] 78.21 59.66 54

D1=2 89.8 50.42 51

D1=3 91.44 57.98 49

D1=4 91.18 65.55 66

D1=5 91.81 66.39 73

(D1=2) + DL 88.41 81.51 83

(D1=3) + DL 89.55 82.35 87

(D1=4) + DL 90.05 85.71 86

(D1=5) + DL 90.81 86.55 87

(D1=5) + DL +CONSH 90.3 89.92 88

Consistency [13] NA 72 NA

Complex filter [14] NA 90 NA

DLjiBajiAjiconsCPji

block ),(&),(:),(minarg,

16

TABLE 2

THE ORIENTATION CONSISTENCY CONSTRAINS

Response level CONSH

a 0.0906<cons<0.86

a-1 0.16<cons<0.7

a-2 0.09<cons<0.85

a-3 0.5≤cons<0.87

TABLE 3

THE AVERAGE EXECUTION TIME OF THE CP DETECTION METHODS (IN

MS).

Method and the code

language

FVC2002

DB2A

800 images

FVC2004 DB1

120 Arch

images

NIST4

100

images

Edge map based [15]

(MatLab)

96 105.8 97.5

Proposed method (Matlab) 36.7 35.6 19.4

TABLE 4

THE ARITHMETIC OPERATIONS USED BY THE CP DETECTION METHODS

PER BLOCK.

CP detection Methods Arithmetic operations

cond add & sub multi trig

Edge map based [15] 8+2 n2 5+4n n2 2 n2

Proposed method 2n+39 4n-5 - -

7 CONCLUSION

The CP conditional filter detects effectively the CP in the non arch types, while when combined with a developed DL con-ditional filter and applying constrains on the orientation con-sistency, it detects the CP in the arch types, too. The condi-tional filter-based method is characterized by its simplicity of design that would be appropriate for the hardware imple-mentation. In addition, the proposed method is faster than the edge map based method by a factor of 3.2, which makes it suitable for the use in the real time environment systems.

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