Adaptive Histogram Modification for Enhancement the Visual Quality of Latent Fingerprints

ROUMEN KOUNTCHEV

Radio-communications Dept. Technical University of Sofia

Bul. Kl. Ohridsky, 8 Sofia 1000, BULGARIA

VLADIMIR TODOROV T&K Engineering Co.

Mladost 3, POB 12 Sofia 1712

BULGARIA

ROUMIANA KOUNTCHEVA T&K Engineering Co.

Mladost 3, POB 12 Sofia 1712

BULGARIA

Abstract: In the paper is presented new method for histogram modification aimed at the enhancement of the visual quality of latent fingerprints. Specific for the method is that the most important visual information is retained because in result of the processing some of the brightness values in the original image change their places in the histogram while other brightness values, corresponding with the image background, are merged. The image obtained in result, answers the requirements of the ISO/IEC SC37 standard and provides better abilities for further image processing such as fingerprints recognition and identification. The main advantage of the new method is that unlike other methods for fingerprints enhancement, based on some kind of image filtration or contrast enhancement, the presented new method improves the visual quality of the processed image, retaining the fingerprint minutiae unchanged. Key words: Histogram modification, contrast enhancement, fingerprints processing 1 Introduction The security threats nowadays require new advanced concepts to be elaborated and implemented as feasible responses to these challenges. The most reliable systems for achieving positive identification (authentication) are based on biometrics. As it is widely known, only three physical characteristics of the human body have the uniqueness necessary to provide absolute positive identification rather than a high probability of positive identification: the fingerprint, the retina of the eyeball and the iris of the eyeball. Most popular are the fingerprint systems, which vary from single thumbprint, full fingerprint, multiple fingerprints, palm print, and hand topography. The archiving of the fingerprint images in the existing databases requires their reliable saving in digital form with retained visual quality.

The standard ISO/IEC SC37 specifies the requirements for the visual quality of the fingerprint images [1]. The up-to-date fingerprint sensors generate high-quality images, which meet the standard requirements on resolution, contrast and signal-to-noise ratio. More problems arose when latent fingerprints are processed, because in many cases they have insufficient contrast.

Significant number of algorithms had been developed for image processing, aimed at the contrast enhancement of natural halftone images [2-8]. The quality improvement is usually performed transforming the brightness of every pixel in accordance with a function of some kind. The main functions could be classified as follows: 1) Statistical transforms based on histogram equalization and modification, and transforms, based on local statistics information; 2) Linear, piecewise linear and non-linear (logarithmic, hyperbolic, exponential) transforms, etc.; 3) Membership functions for contrast adaptation by minimization of the image fuzziness, fuzzy histogram hyperbolization, etc.; 4) Transforms, based on the global image histogram data or on the local histogram statistics. On the basis of these methods, many software tools had already been developed for contrast enhancement of natural halftone images, but when applied on fingerprint images the results are not as good. The main difficulties with this image class are a consequence of the requirement all the fingerprint minutiae to be retained, because this is of high importance for the successful recognition and identification (for example, even the sweat pores are accepted as evidence in court and this requires their retaining).

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In this paper is presented new method for adaptive histogram modification aimed at the enhancement of the visual quality of the processed images, retaining the whole available information. The paper is arranged as follows: in Section 2 is presented the algorithm for histogram modification and image segmentation, in Section 3 are given experimental results, compared with some of the widely known methods for local and global histogram equalization and are pointed the main advantages of the presented method. In the Conclusion are presented the main applications of the method and its future development. 2 Algorithm for adaptive histogram modification Specific quality of the latent fingerprint images is their insufficient contrast and (correspondingly) the narrow range of brightness levels. The ISO/IEC SC37 working draft on the fingerprint image data standard [1] specifies the main requirements for the fingerprint image quality as follows: latent print scanners should have minimum resolution of 1000 dpi; the signal-to-noise ratio should be equal to or greater than 125; the dynamic grayscale range of the image data should be 200 gray levels for at least 80% of the captured image, and 128 gray levels for at least 99% of the captured image. The typical latent fingerprint images do not answer these requirements. In Fig. 1 is presented an example fingerprint image (300x300 pixels, 8 bpp) and in Fig.2 – its histogram.

Fig. 1. Test fingerprint image

The largest maximum in the histogram corresponds with the main part of the image, i.e. the fingerprint data. The second maximum (at the right side in Fig.2) corresponds with the image background. The histogram shows that the significant brightness levels corresponding with the fingerprint are placed

in the range 100-200, i.e. the requirements for the number of available gray levels and the dynamic grayscale range are not satisfied. The image histogram modification, presented below, offers a flexible tool for solving the problem.

Fig.2. The histogram of the test image with set

boundary points

The description of the operations, which comprise the algorithm for adaptive histogram modification, follow below. 2.1 Calculation of the image histogram The calculation of the image histogram permits to define the minimum and maximum brightness values in the processed image and to evaluate the quality of the processed image.

The image histogram H(x) is calculated in accordance with the relation:

H(b) for b = 0,1,..,bmax, (1) where (bmax+1) is the number of gray levels. 2.2 Image segmentation The positions of the maximums in the image histogram are used to set the points, which define the main objects in the processed image, i.e. the fingerprint and the background (Fig. 2) and for this, adaptive segmentation is performed. The boundaries of the segments are defined by the points, which separate the parts of the histogram, corresponding with the main objects in the processed image (in this case - the fingerprint and the background). These points are defined analyzing the image histogram. As it is seen from Figure 2, there are two maximums (corresponding with the two objects) and many brightness values, which are not used in the processed image. The image segmentation is performed following the algorithm,

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presented below. Three important assumptions are made:

The image represents a monochrome background with dark fingerprint on it;

The minimum difference between the mean brightness values for the object (fingerprint) and the background is δ;

Sequences of brightness values, represented in the image histogram with small number of pixels only (less than 0,05% of the total number of pixels MxN in the image for a single brightness value), are merged as separate objects. The main steps of the algorithm for image segmentation are: Step 1. Calculation of the brightness level b=α1, for which h(α1) = max in the range 0≤b≤bmax. Here α1 is the mean brightness value for the background. Step 2. Calculation of the brightness level b=α2, for which h(α2)=max in the range 0,0005(MxN)≤b≤(α1-δ). Here α2 is the mean brightness value for the object (the fingerprint). Step 3. Detection of the brightness levels b=α3, for which h(α3)≤0,0005(MxN) in the range 0≤b≤α2. Step 4. Detection of the brightness levels b=α4, for which h(α4)≤0,0005(MxN) in the range α2≤b≤bmax. Note: This step is not obligatory, but it is very useful if such areas exist in the image histogram. Step 5: Calculation of the histogram modes for α2≤b≤(α1-δ), after which for every mode (sub-range of h(b), which has a local maximum) is calculated the corresponding area:

S for i = 1,2,..,P. (2) ∑1+

==

i

i

β

βbi )b(h

Here iβ and 1+βi are the brightness levels, which define the boundaries of the sub-range “i”. These boundaries coincide with the closest minimums on the left and on the right of the local maximum; P is the number of sub-ranges (respectively, the modes); S1 corresponds with the mode of the fingerprint between the range boundaries, and SP – with the mode of the background between the boundaries: from up to β (in case that another range had been defined in step 4, the upper boundary is a brightness value, lower than b

Pβ maxP b=1+

max. Step 6: Calculation of the histogram modes

for α1≤b≤α2, after which for every mode (sub-range of h(b), which has a local maximum) is calculated the corresponding area (as in Eq. 2).

Similar calculations are performed for the range α1≤b≤bmax, if such range had been defined.

Step 7: The value of P (obtained in Steps 5 and 6) is analyzed:

- For P>4 is performed recursive merging of the modes in the range β ≤b≤ in correspondence with the relation:

2 Pβ

1−≤1−1−∪1−1−>1−1−∪1−

=∗

1+∗

1−∗

1+∗

∗1+

∗1−

∗1−

∗∗

),k(S)k(S if ),k(S)k(S);k(S)k(S if ),k(S)k(S)k(S

iiii

iiiii (3)

for i=1,2,..,P(к), where S is the area of the merged mode “i“ in the iteration ”k”, and P(k) is the number of the modes for the iteration “k” if the condition P(0) = P is satisfied. The modes merging is ended in the iteration “k”, for which P(к)=4 and with this the analysis is finished. Values higher than 4 mean that in the image had been detected too many objects (not just a fingerprint and a background).

)k(i∗

- For P=4 are defined the values of β , and , and with this the analysis goes to the end.

2 3β

Pβ- For P<4 should be calculated new boundaries

and , with which the analysis goes to the end.

Pβ)δα(β <+= 2∗2 Pβ)δα(β <−= 23

In result are obtained the areas, corresponding with the 2 main objects of the brightness histogram, as follows:

• For the fingerprint - from β up to (or - i.e. the upper boundary of the mode S );

3 2β)k(∗

2β∗1

• For the background - from β (or β - i.e. the lower boundary of the mode S ), up to b

P

)k(

∗P

Р∗

max. In the range ( are the gray levels,

which correspond with the noises in the background. The values β and β are used in the next stage of the image processing aimed at the suppression of the noise influence on the compression ratio, retaining the text information.

)ββP 2−

2 3

2.3 Evaluation of the fingerprint image quality In result of the image segmentation is obtained the following result (Fig.2): the sub-range, placed between brightness values 0 and k1, represents the part of the histogram, which contains negligible number of image pixels only; the separating points k1 and k2 which define the main part of the example fingerprint image, are at positions 100 and 200 because the brightness level 100 is the beginning of the sequence of “significant” brightness values in the image histogram (α3), and the brightness level

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200 is the outlined minimum before the second maximum; the points k2 and k3 (brightness range 200-235) define the boundaries of the second object in the image, which corresponds with the background. The brightness levels in the range 235-255 represent the part of the image with highest brightness and practically do not contain significant information in this case (there are no pixels with such brightness in the image). The quality of the fingerprint is evaluated in the scope of the ISO/IEC SC37 standard requirements, concerning the dynamic grayscale range. For the presented test image the dynamic grayscale range (placed between the brightness values 99-200) is less than 40% of the histogram. 2.4 Image histogram modification The image histogram is modified so that to retain the brightness levels, which correspond with the main information in the image and to suppress the remaining data. The histogram modification is performed in 2 parts. The first part is used to limit the number of brightness levels in the image background, retaining the most significant information (the object), and the second part is aimed at the contrast enhancement of this most significant part of the image.

Accordingly, the histogram of the example image is divided in 5 ranges:

- Range 1 (brightness levels 0-99) – the brightness levels, which are not used;

- Range 2 (brightness levels 100-199) – the brightness levels, corresponding with the fingerprint;

- Range 3 (brightness levels 200-234) – the brightness levels, corresponding with the background of the latent fingerprint

- Range 4 (brightness levels 235-249) – the brightness levels, corresponding with the background, but which do not contain important information;

- Range 5 (brightness levels 250-255) - the brightness levels, whose brightness information is neglected (too small number of pixels with such brightness in the processed image). Then the histogram is processed as follows:

Range 1 - represented with 1 brightness level only (brightness = 0). In the example test image there are no brightness values in this range;

Range 2 - all brightness levels are retained; Range 3 - represented with 4 brightness levels,

placed at equal distances; Range 4 - represented with 2 brightness levels,

placed at equal distances;

Range 5 - represented with 1 brightness level only (brightness bmax = 255).

The image, obtained in result of the 5-part histogram modification is shown in Fig. 3 and its histogram – in Fig. 4. It is easy to see that the visual quality of the example image is not changed, while in result of the processing ranges 3, 4 and 5 are represented with smaller number of brightness levels, gathering several values in one. The processing continues with the second step - the contrast enhancement of the most significant part of the image. For this, the part of the histogram, which contains the main object (the fingerprint), placed in Range 2, is stretched, and the remaining ranges are skewed correspondingly. To perform this, is assumed that the image histogram comprises 3 main parts, i.e. part A, which contains the brightness values in the range 0-99 (Range 1), part B, which contains the brightness values in the range 100-199 (Range 2), and part C, which contains the remaining brightness values in the range 200-255 (i.e., the merged Ranges 3, 4 and 5). The separating points for these three parts are k1 and k2.

Fig. 3. The test image obtained after 5-part histogram modification (visually same as Fig.1)

The contrast enhancement) is performed changing the brightness level, b for every pixel in these three parts in accordance with a transformation table, as follows:

(4)

≤<≤≤<≤

=.b bk if )b(g;k bk if )b(g;k b 0 if )b(g

)b(g

max2C

21B

1A

Here gA(b), gB(b) and gC(b) are the corresponding relations for the brightness transformation in the parts A, B and C. In order to improve the fingerprint image quality, the end points k1, k2 of the part В are widened (stretched) up to

and skewing 011 ¡Ý)δ -k( 255+ 22 ¡Ü)δk(

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correspondingly the parts A and C. In this case δ1 and δ2 are parameters, which influence the contrast enhancement for the part B and as a result - the change of the contrast range for the remaining two parts, A and C.

Fig. 4. The histogram of the image in Fig. 3

Each table for brightness transformation is defined in accordance with the condition for histogram equalization for the corresponding part (A, B or C) with changed (stretched or skewed) dynamic range:

(5) ,)l(h)δ-k()b(g ‡”b

lAA

0=11=

(6) ‡”b

δklBB )δ-k()l(h)δδk-k()b(g

11−=112112 +++=

(7) )δk()l(h)δ -k -b()b(g ‡”b

δklCmaxC 22

+=22 ++=

22

In particular, in case that the histogram of the corresponding part is constant, i.e. for:

;-k0,1,..,bfor k

)b(h 1A 1=1

=1

(8)

;k1,..,k,kbfor k-k

)b(h 211B +=1

=12

(9)

,b2,.,k1,kbfor k-b

)b(h max22max

C ++=1

=2

(10)

the table for the brightness transform for each part is linear and the relations are defined as:

;bkδ -k)b(gA

=

1

11 (11)

;k-k

kδkδb

k-kδ-δ-k-k

)b(g B

++

=

21

2112

21

2121 (12)

. (13) k-k

kδb

k-kδ-k-k

)b(gmax

max

max

maxC

+

=

2

2

2

22

In result, the brightness levels in the part B are stretched in accordance with a linear relation and corresponding inverse operations are performed for the two remaining histogram parts (0, k1-1) and

(k2+1, bmax), i.e. they are skewed. The image obtained in result of the processing, is shown in Fig.5, and its histogram – in Fig.6.

Fig. 5. The final image

Fig. 6. The histogram of the image in Fig. 5 In case that the histogram has only one

maximum, the image segmentation could be performed with the 3-segment algorithm, presented above. 3 Experimental results The performance of the presented method was checked with special software, TKView (developed by the research team), which implements the algorithms, presented above. For the testing was used significant number of fingerprints (more than 100). All images were of size 300x300 pixels, grayscale (8 bpp). The 3-part histogram modifi-cation was performed setting the widening of the meaning part of the image as a percent (in the range 80-90), i.e. the “meaning” part of the image (Range 2) was stretched up to 80% from the possible 255 values. In order to make the visual changes in the processed image as small as possible, the highest brightness values for each range were retained unchanged. Two of the test images and the obtained results are shown below (Figs. 7, 8).

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Fig.7. The original test image

Fig.8. The processed test image

4 Conclusion The experimental results proved the method efficiency. Compared with other methods for contrast enhancement [5,8], the new method has specific advantages:

- Lower computational complexity: compared with other methods for contrast enhancement, based on image filtration, the computational complexity of the presented method is lower;

- Adaptation: The method is adaptive and the image processing is performed depending on the results from the image histogram analysis, which permits the values of the participating parameters to be set automatically.

Unlike other methods for contrast enhancement, which are based on image filtration, there are no changes in the frequency domain of the processed image and in result all the available information is preserved. The visual quality of the images processed in accordance with the new method is better than those, obtained with other methods, based on contrast enhancement or local

equalization. Some examples, obtained with Corel Photo Paint 10, are shown in Fig. 9.a,b. In both examples the processed image quality is worse than that in Fig. 5.

a b Fig. 9. The test image from Fig.1: a) after contrast enhancement; b) after local equalization Acknowledgement This paper was supported by the National Fund for Scientific Research of the Bulgarian Ministry of Education and Science (Contract BY-TH 202). References: [1] International Standards Organization, ISO/IEC

JRC1/SC37. Working draft, Biometric data interchange formats-Part 4: Finger image data, Geneva, Switzerland, 2004.

[2] Al Bovik (Ed.). Handbook of Image and Video Processing. Academic Press, 2000.

[3] S. Bow. Pattern Recognition and Image Preprocessing, Marcel Dekker, 2002

[4] D. Fuentes, D. Mostefa, J. Kharroubi, S. Garcia-Salicetti, B. Dorizzi, G. Chollet. Identity verification by fusion of biometric data. Proceedings COST 275 Workshop on the Advent of Biometrics on the Internet, Nov. 2002, pp. 83–86.

[5] A. Rowberg, B. Malcolm. Distortion-free Image Contrast Enhancement, “EuroPACS-MIR 2004 in the Enlarged Europe”, pp. 357-360.

[6] G. Sharma (Ed.). Digital Imaging Color Handbook, New York, CRC Press, 2003.

[7] Y. Kim. Contrast Enhancement Using Bright-ness Preserving Bi-Histogram Equalization. IEEE Trans.on CE, Vol. 43, No. 1, 1997,pp.1-8.

[8] B. Jahne. Computer Vision and Applications. Academic Press, 2000.

[9] I. Young, J. Gerbrands, L. van Vliet. Image Processing Fundamentals. Delft, Netherland, 1996.

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