Fingerprint data processing method

The present invention provides a method of processing an unknown physical fingerprint for use in a data processing system to verify/identify an unknown fingerprint. The method involves obtaining an unknown fingerprint image that includes images of ridges, binarizing the images of ridges in the unknown fingerprint image, generating an image with fused ridges by expanding the images of ridges in the binarized image, shrinking the image with fused ridges to create a scaled-down image, and masking a first portion of the unknown fingerprint image with the scaled-down image so as to create a modified image of the unknown fingerprint.

FIELD OF THE INVENTION 
The present invention relates to fingerprint verification, especially to a 
method for extracting a reference point of a fingerprint image and to a 
method of eliminating false fingerprint minutiae that may have been input. 
BACKGROUND OF THE INVENTION 
The minutiae of a fingertip are the ends and bifurcations of the ridges of 
a fingerprint. A fingerprint verification method using the minutiae 
characteristics requires a large amount of data and is dependant on the 
data being reliable. The precise input of the minutiae is a prerequisite 
for the successful operation of such method, and the verification depends 
upon the quality of the fingerprint image. 
A fingerprint image inputted through a camera or other such device usually 
does not consist of a total fingerprint, but instead usually consists of a 
partial image of a fingerprint. End points are therefore generated at the 
points where ridges are cut by the periphery of the image. Given that such 
end points are not part of the minutiae, they should be distinguished from 
the real minutiae characteristics. An established method for performing 
such distinguishing is not known. 
For a fingerprint verification using the minutiae characteristics , the 
determination of which part of a fingerprint image will be the object for 
the comparison is an important problem because the choice of the object 
area will affect the results of the verification. Therefore, in many 
cases, the center or core of a fingerprint image is adopted as the 
reference point of the object area. However, the concept of identifying 
the center point has not yet been refined. 
SUMMARY OF THE INVENTION 
An object of the present invention is to provide a fingerprint data 
processing method which can distinguish and reduce false end points 
generated around the frame of the image when fingerprint minutiae are 
input. Another object of the present invention is to provide a method for 
extracting a reference point which can easily be determined. 
According to the present invention, an fingerprint image which has been 
inputted is swelled and then shrunk to a flat image that is smaller than 
the original fingerprint image. The flat image is used as a mask for 
extracting the significant area of the fingerprint image to be examined. 
By so doing, it is possible to reduce the false end points which are 
created around the frame of an image and to input only real minutiae. 
According to the present invention, a reference point is extracted from a 
distribution of a number of cross points where each scan line crosses the 
edge of a configuration area adjacent to an background area. The maximal 
peak of the distribution is deemed as the reference point.

PREFERRED EMBODIMENT OF THE PRESENT INVENTION 
Hereinafter, an embodiment of the fingerprint data processing method 
according to the present invention is described with reference to the 
attached drawings. 
As is show in step 1-1 of FIG. 1, a fingerprint image is input by a 
well-known method such as the total reflection method. In step 1-2, noise 
is reduced by a median-filter or by some other method. The image of the 
ridge is made clear by performing shading compensation in step 1-3 and the 
image is binarized into the optimal value in step 1-4. Ridge thinning is 
executed and the thinned image is recorded in step 1-5. In step 1-6, end 
points and branch points are extracted from the thinned image and the 
image is recorded in a memory. 
The thinned image is then swelled a predetermined number of times and the 
ridges are fused in step 1-7. FIG. 2 shows a thinned image I in which 
false end point "P" is generated around the frame of the image, and an 
image II which has been swelled and fused on a thinned image. Image II 
completely covers image I and comprises all the ridges and minutiae of 
image I. 
The image from step 1-7 (image II in FIG. 2) is shrunk a predetermined 
number of times in step 1-8. As shown in FIG. 2, shrunk image III is 
smaller than thinned image I. Image III does not include the area around 
the frame of image I. The area around the frame can therefore be extracted 
from image I by using image III as a mask image. 
The number of times the swelling of step 1-7 and the shrinking of step 1-8 
are performed is determined by experience and varies with the size and 
resolution of the image. It is also possible to ascertain the completion 
point of the fuse according to the decrease of the change of area by 
determining the area every time on every swelling. The completion point of 
the shrinking can be ascertained by the steps of: i) determining Feret's 
diameter of the masked image, ii) comparing this to the Feret's diameter 
of image I, and iii) judging whether shrinking is complete according to 
the decrease in the ratio of the Feret's diameters of the masked image and 
of image I. 
As described, false end points are surely reduced and a precise minutiae 
network can be generated because the mask reduced the area around the 
frame of the original image. 
Another embodiment which is concerned with reference point extraction is 
shown in FIGS. 3 to 6. 
FIG. 3 shows a fingerprint image as a model. Ridge "R" is shown by a 
configuration brightness ("1", for example), and other parts are shown by 
a background brightness ("0", for example). The steps of noise reduction, 
shading compensation, and determination of the optimal threshold are 
performed as discussed above in order to obtain the binary image. By 
scanning the fingerprint image in X and Y directions, the cross points of 
the scan line and the ridges are obtained. In FIG. 3, scan lines "L1" and 
"L2" are shown. 
There are two patterns of ridges, arch-type and whorl-type. As for the 
arch-type, experiments show that the center part will be apparent from the 
distribution of "cross points". The cross points of the edge of ridge "R" 
and scan lines L1 and L2 are adopted as the "cross points" here. The 
representative value of the number of ridges can be obtained directly from 
a binary image without thinning ridges and the processing can be performed 
in high speed. 
Either one or both of the points at which the brightness changes (i.e., 
either from background brightness to configuration brightness or from 
configuration brightness to background brightness) can be adopted as the 
edges of the ridges. When both are adopted, the number of ridges through 
which each scan line passes across is twice the number of edges. The 
characteristics of the distribution of the number of edges is equivalent 
to that of the ridges. 
The number of cross points of a scan line and a ridge tends to distribute 
as shown in FIG. 4. It makes a peak around the center of a fingerprint. 
The coordinates of the point with the maximal value "LM" in both the X and 
Y directions is settled as a reference point. 
In FIG. 5, there are two peaks: the larger sharp peak of LM1 and the 
smaller, wider peak of LM2. When the distribution of LM1 is less than the 
predetermined value, LM2 will be adopted as the reference value. Here, the 
distribution is judged by the width of peak W1 and W2 by cutting the peak 
at threshold "Nt". In this way, the effect of noise on the determination 
of the reference point can be prevented by ignoring the peak with narrow 
distribution. 
FIG. 6 shows a peak with a type of noise which causes the jagged or 
non-smooth distribution of cross points. In this case, the maximal value 
is judged after smoothing correcting the distribution which was shown by 
the jagged line. 
The processing described is high speed because the cross points of edges of 
ridges and scan lines are counted. It is possible to overcome the effects 
of noise on the determination of a reference point by only adopting the 
peak with equal to or more than the predetermined distribution. 
According to the present invention, an inputted fingerprint image is 
swelled and then shrunk to a flat area smaller than the original 
fingerprint image. The flat image is used as a mask for extracting 
significant area of the fingerprint image to be examined. Therefore, it is 
possible to reduce the false end points around the frame of an image and 
to input only real minutiae characteristics by the present invention. In 
addition, the invention allows a reference point to be extracted from a 
distribution of a number of cross points where each scan line crosses an 
edge of a configuration area adjacent to an background area. The maximal 
peak of the distribution is deemed as the reference point. Consequently, 
the effect of noise on the reference point can be minimized.