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A New Approach to the Analysis of Fingerprints


Conventional methods of analysing fingerprints require acquisition of their images via a sensor array which is usually a CCD or, less popularly these days, a tube sensor. In either case, the image is formulated in the system by horizontal and vertical scanning, and converted into Cartesian arrangements of pixels in the subsequent processing. Standard image processing routines, such as edge detection and thinning, are used to extract the salient features so that comparisons with previously-stored data can be facilitated. However, there is an argument that such an approach is quite unnatural. How can this be so? Consider the fact that, in general terms, a fingerprint shows circular characteristics. Why should it be constrained, when analysed, to fit into a rectangular world? The study undertaken at Bradford shows that a different approach to fingerprint analysis can be taken, resulting in certain advantages over conventional methods.


The problem mainly associated with Cartesian coordinate imaging is in the measurement of diagonal distances and velocities. Spatial resolution along the diagonals is lower compared to measurements along the two major axes. Consider a different geometry: if an image is broken down into hexagons, instead of rectangles, then it is possible to use 3 major axes. Furthermore, each pixel is equidistant from each of its 6 nearest neighbours in the hexagonal format. Consequently, distance and velocity measurements are facilitated. Clearly, the outside world being Cartesian, in terms of sensors, computer memory, and the basic television system in general, leads to certain compromises when adapting to the new approach. Nonetheless, and perhaps surprisingly, this process is not difficult, thus, reduction of necessary stored data is possible, leading to improved processing of circularly-symmetric data ranging from 13.4% to 50% [1]. Pre-processing is an essential part of an automatic fingerprint identification system, because if the fingerprint is not of an acceptable standard automatic fingerprint recognition becomes extremely difficult. The main reason for this is that normal methods use the small unique features known as minutie in the image to identify the fingerprint. The pre-procrssing procedure consists of four stages:


A fingerprint image contains narrow ridges separated by equally-narrow background valleys. This may be corrupted by various kinds of noise, causing breaks in the ridges, joins between them and overall grey scale intensity variation. In order to enhance the image, filtering is applied, which is designed to increase the contrast between ridges and valleys. The filter should also enhance data along the same orientation as the ridges within the local area of each pixel. The contrast enhancement should depend only on local differences in intensities between ridges and background. That is, the filter should perform adaptively to regional differences in average intensity. The fingerprint image may be transformed to a directional form that represents the local orientations of the ridges [1,2]. Six directions are chosen to determine the local ridge orientation, as shown in Figure 1. A filter for one direction is designed, and filters for all other directions are generated by rotating this basic filter. The fingerprint ridges vary between one and three pixels. A filter mask using six nearest neighbours is chosen (Figure 2). The middle row has coefficients to amplify the ridges.

By looking into the block directional image, the appropriate directional filter may be selected. This filter is used for convolution of the fingerprint image within the region.

Adaptive Binarisation

The aim of segmentation of fingerprint images is to separate the clear fingerprint area from the non-fingerprint area. An acceptable segmentation technique should have the following characteristic:

The histograms of fingerprint images are not bimodal. Hence, thresholding and segmentation of these images are nontrivial. A form of adaptive average thresholding has been found to be most suitable for this application. By selecting a suitable region size, computing its average and setting the ridges and valleys to distinct grey levels within the image, uneven impressions can be removed with considerable success. For best results, a window that encompasses one period of the fingerprint image (ie one ridge and one valley) is chosen.

Noise removal

The binarisation algorithm may introduce noise in the form of small holes in and around the ridges. In order to remove these, a number of morphological operators are convoluted with the image, as shown in Figure 3.

The first template fills in isolated holes within the fingerprint ridges. The second and third templates, along with five rotations fill in gaps along the ridge edges. The templates switch the centre pixel from "0" to "1" if all the conditions in that template are true.


Generally the thickness of the fingerprint ridges varies because of bad inking, improper printing, etc. In classifying the prints and extracting the features from the ridges, it is necessary to reduce the thickness of the ridges to unity. In this approach the thinning can be seen as the result of an iterated compression process. At every step, contour pixels are tested using suitable removal criteria. This causes some internal pixels at the next step. Removal operations are iteratively applied until no further pixel is removed. Due to the symmetric erosion, the skeleton becomes centred within the original shape and is of unit width everywhere. Six operators shown in Figure 4 are passed over the image along 0, 60, 120 degree orientations. The removal criterion is determined by the crossing number and the sum of the nearest neighbours, respectively (2). The criterion preserves the connectivity of the thinned image whilst the second stops any unit limbs from disappearing. Each iteration is subdivided into six Note that templates (a)-(f) ensure that only boundary pixels are removed and hence no hole is formed during any pass of the iterations. The thinning tends to leave small noise spurs when the input image boundary is noisy. These can be eliminated by deletion [5].

Methods and results

Fingerprints are inked and impressed on paper. Physical prints are captured and digitised into an array of size 512 by 512. In order to simulate a hexagonal grid, the image is subsampled, as in [3]. Figure 5 illustrates the hexagonal fingerprint image of size 256 by 256. Figure 6 shows the result after filtering, binarisation, noise removal, and thinning has been applied. The ridges have been reduced to the width of one pixel and the connectivity has been preserved.


An alternative sampling strategy to the Cartesian approach is hexagonal sampling. It offers greater angular resolution than the former, as its nearest neighbours are separated by sixty degrees instead of ninety. Also, the hexagonal grid has an appealing isotropic character, due to the fact that each cell is equidistant from its nearest neighbours. It has been found that algorithms which pre-process fingerprints on a hexagonal grid can be implemented quickly and efficiently; typically 20% faster with fewer data points. The method therefore incorporates a form of real data compression intrinsically.


  1. Mereseau R, Hexagonally-sampled two dimensional signals Proceeding of the IEEE, pp 927-952, 1979.
  2. Mehtre, B M, Murthy N N, Kapoor S and Chatterjee B, Segmentation of fingerprint images using directional image Pattern Recognition 20, pp 429-435, 1987.
  3. O'Gorman L and Nickerson J V, An approach to fingerprint filter design Pattern Recognition 22, pp 29-38, 1989.
  4. Lester L N and Sandor J, Computer graphics on a hexagonal grid Computer Graphics vol. 8
  5. Deutsch E S, Thinning algorithms on rectangular, hexagonal and triangular arrays Communications of the ACM, Vol. 15, pp 827-835, 1972.
  6. Gonzalez R C and Wintz P, Digital Image Processing, pp 351-366, 1987.
  7. Golay E, Hexagonal parallel pattern transformations IEEE Trans on Comp, Vol. 19, pp 733-740, 1969.
  8. Davis E R and Plummer P N, Thinning Algorithms: A critique and a new methology Pattern Recognition, Vol 14, pp 53-63, 1980.

Roger J Green and Andrew Fitz
Electronic Imaging and Media Communications Unit
University of Bradford