2010 International Conference on Pattern Recognition
Improved Fingerprint Image Segmentation and Reconstruction of Low Quality Areas Krzysztof Mieloch, Axel Munk Institute for Mathematical Stochastics University of Goettingen Goldschmidtstrasse 7, 37077 Goettingen, Germany {mieloch, munk}@math.uni-goettingen.de
Preda Mih˘ailescu Mathematical Institute University of Goettingen Bunsenstrasse 3-5, 37073 Goettingen, Germany
[email protected]
Abstract—One of the main reason for false recognition is noise added to fingerprint images during the acquisition step. Hence, the improvement of the enhancement step affects general accuracy of automatic recognition systems. In one of our previous publications we introduced hierarchically linked extended features – the new set of features which not only includes additional fingerprint features individually but also contains the information about their relationships such as line adjacency information at minutiae points or links between neighbouring fingerprint lines. In this work we present the application of the extended features to preprocessing and enhancement. We use structural information for improving the segmentation step, as well as connecting disrupted fingerprint lines and recovering missing minutiae. Experiments show a decrease in the error rate in matching. Keywords-fingerprint recogntion, preprocessing, enhancement, line connecting
I. H IERARCHICALLY LINKED FEATURES In [1] we introduced the concept of additional fingerprint features – hierarchically linked extended features – which improve the performance of fingerprint recognition methods. The fingerprint’s crucial information is extracted into a structure containing minutiae, segments (fingerprint lines) and border points (places where fingerprint lines hit the background1) – Fig. 1(c). In our extraction method the information contained in both ridges and valleys (or in other words white and black lines) is evaluated, as well as the dual information, that is the interference between white and black areas. Generally, bifurcations in one area correspond bijectively to endings in the other area – Fig. 2(a). Natural exceptions appear only at singular points – Fig. 2(b); further exceptions are consequences of noise. One of the features contained in the extracted structure, which plays a major role for our new segmentation method is the neighbourhood information. For segments the neighbouring lines are identified for each extracted line, or in
(a) Original
(b) After segmentation
(c) After extraction
(d) Connected components
Figure 1. 1 The
background includes not only the surrounding area of the imprint, but also areas disturbed by noise as well as those devoid of clear fingerprint structure within the imprint.
Unrecognized Copyright 1051-4651/10 $26.00 © 2010 Information IEEE DOI 10.1109/ICPR.2010.309
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Image 10 4 from FVC2000 db2.
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(a) A bifurcation in white with the corresponding ending. Figure 2.
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(b) The exception for a singular point.
Duality principle
case of outermost segments, the background is marked as the neighbour. Similarly, for each border point its neighbour points on both sides, if present, are determined. The extracted structure can be regarded as a graph in which minutiae and border points are vertices and segments are edges between them – GM = (VM , EM ). As D we will denote the set of all connected components in GM – compare Fig. 1(d).
Figure 3.
Low (A) and high (B) quality regions with extracted segments
II. S EGMENTATION The common segmentation methods make use of the fact that the foreground contains a clear structure of ridges and valleys [2]. However, fingerprint images can also contain some ridge-like structures in the background such as a second finger, phalanx impressions, or remains of old skin impressions from a previous acquisition, which results in false segmentations as presented in Fig. 1(b). A. Additional features In order to correct the errors of segmentation algorithms, we will evaluate additional information contained in the extracted fingerprint structure. An input fingerprint image is segmented by a common segmentation algorithm [3] and then the areas classified incorrectly as foreground are identified and marked as background. The following features are used as indicators for an area belonging to the background. Average length of connected components. For each connected component d ∈ D its average length l(s) s∈S(d) ¯l(d) = |S(d)|
Figure 4.
Average component lengths
consistent, e.g. for segment 1 no neighbour was found as possible neighbours are too far away, whereas in the region B the line flow is well preserved and each segment has its neighbour. B. Block averaging Image points are assigned to the background in a blockwise manner. For descriptive features their mean value in each block is computed while quantity features are summed up. Exemplary average values for connected components from Fig. 1(d) are plotted as a mesh in Fig. 4.
is computed. S(d) – set of all segments in the component d. l(s) – length of the segment s. Experiments have shown that the average length of connected components is small in misclassified areas, which is a consequence of the increased number of spurious minutiae in such areas. Number of unvalidated minutiae. As already mentioned, every singular point has a corresponding unvalidated minutia (i.e. a minutia without the corresponding minutia in the dual colour area). Each remaining genuine minutia has its corresponding minutia in the dual area. As there exist at most 4 singular points, a large number of unvalidated minutiae in a region is an indicator for the background. Number of segments with no neighbours. In the disturbed area A in Fig. 3 the neighbour information is not
C. Identifying background With the help of the features described above a classification algorithm can be constructed to discriminate between background and foreground. We have decided to use a linear classifier as it has low computational complexity: y(c, m, s) = ac c + am m + as s + b, where c is the mean of average length of connected components in the block, m is the number of unvalidated 1242 1246
(a) Before Figure 7.
(a) Common method Figure 5. steps
Figure 6.
Connecting lines
Since low quality regions are surrounded by non-disturbed foreground, they can be enhanced or even reconstructed with the help of the information contained in its neighbourhood.
(b) Our improvement
A. Reconstructing broken lines
The image from Fig. 1(a) after binarisation and segmentation
(a) Original
(b) After
We use the extracted information to connect the disrupted lines: from the outermost line towards the centre of the noisy area. Fig. 7 presents the result for a low quality region which has an equal number of segment ends at both sides. Obviously, if a noisy region does not contain any minutia, the number of segment ends at one of its sides is equal to the number at the other side. However, this statement cannot be reversed because even if the numbers at both sides are equal, there might exist minutiae in the low quality region as presented in Fig. 8(a). Such minutiae cannot be reconstructed by our algorithm and thus will be missing. On the contrary, the difference in the number of line ends at both sides of a low quality area is a clear indicator for the existence of one or more minutiae in that region.
(b) Light grey – surrounding area. Dark grey – low quality regions. Image 1000 2 from NIST 4 db
B. Minutiae in low quality regions
minutiae in the block, and s is the number of segments with inconsistent neighbours in the block. And the decision for a block is: remains foreground if y(c, m, s) ≥ 0 block changes to background otherwise
We should take two situations into account: there exist two neighbouring line ends lying in the same colour area – Fig. 8(b) or each neighbour of an end, if one exists, lies in the dual colour area and thus the difference in the numbers of ends at both sides is even – Fig. 8(c). In the first case, any two neighbouring ends lying in the same colour area are connected to make a bifurcation (Fig. 8(d)) and consequently the number of ends at that side decreases by one. After processing of all such neighbouring ends the second condition is fulfilled. In that case the strategy is as follows: in order to find likely connections between the disrupted segments, we make use of the fact that the flow of fingerprint lines is very smooth and thus we try to fit a polynomial curve to points at the ends of the disrupted segments. The connections with the best fit are chosen. Sample fits are presented in Fig. 8(e). We use curves of degree 2 that can be written down in the parametric form as l(t) = a2 t2 + a1 t + a0 , b2 t2 + b2 t + b0 . For the approximation four points are chosen, two on each segment. The residual sum of squares defines the cost of the connection. The lines are connected in such a way that the overall cost is minimised and all lines are connected.
The weights ac , am , as and b are determined during a supervised learning phase with the help of images in which the background has been marked manually. A sample result is presented in Fig. 5. The small area of the second finger has been falsely attached to the foreground since the area has the ridge-valley structure which cannot be distinguished from a genuine foreground. Another error is the assignment of the small regions in the left and right top to the background. However, these are regions on the margin and thus not much relevant for the matching. III. L OW QUALITY REGIONS Background, regarded as the area in which no information is contained, can be divided into: the surrounding area around the fingerprint and low quality regions within the foreground. See Fig. 6. 1243 1247
R EFERENCES [1] K. Mieloch, A. Munk, and P. Mihailescu, “Hierarchically linked extended features in fingerprints,” in Biometrics Symposium, 2008. BSYM ’08, Sept. 2008, pp. 47–52.
(a) Hidden minutiae in a low quality region
[2] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition. New York: Springer, 2003. [3] A. M. Bazen and S. H. Gerez, “Segmentation of fingerprint images,” in Proc. ProRISC 2001 Workshop on Circuits, Systems and Signal Processing, 2001, pp. 276–280.
(b) Low quality region with odd (c) Low quality region with even difference in the number of segment difference in the number of segment ends ends
(d) Corrected situation (b)
(e) Fitting of polynomials
Figure 8. Table I E QUAL ERROR RATES
FVC2000 db3 FVC2002 db2
without 6.892% 1.028%
with 5.272% 1.012%
If a segment end has been connected to two other segment ends, a bifurcation is placed in the middle between the left and the right side of the low quality area since the exact position cannot be determined. IV. R ESULTS FOR MINUTIAE MATCHING In order to check the quality of the proposed methods we took a commercial fingerprint recognition software from the top ten in the FVC 2006 [2] ranking list. We chose two databases from the FVC databases, an easy one – db2 from the year 2002 and one containing more noisy images – db3 from 2000. The equal error rates were computed by matching the fingerprint impressions according to the FVC protocol, which results in 2800 genuine and 4950 impostor pairs for each database. We compared the error rates in case of minutia matching with and without the application of the proposed methods to the input fingerprint. The results are presented in Table I. A distinct improvement for noisy images from the database db3 can be observed. Moreover no significant change of the error rate for the database db2 shows that proposed methods do not influence the results for images of good quality. V. C ONCLUSIONS The reported methods for fingerprint enhancement utilise not only the spatial information contained in the image but also the structural information extracted into the hierarchically linked extended fingerprint structure. The tests have shown an increase in performance as the result. 1244 1248
