A technique for fingerprint comparison based on template matching is presented. A digitised greyscale image is initially pre-processed, from which the template is derived. The use of "<I>don't care</I>" states in the template, which inhibit pixel comparisons, prevents environmental variations and noise from adversely affecting correlation results with subsequent images. The novelty of this approach is that although template matching is a mature method of pattern recognition, there are no reported successful attempts that solve the problem of fingerprint comparison using this technique. The fingerprint reference comprises a set of sub-templates in order to overcome localised skin stretching. These are individually correlated with the processed binary image. Significant correlation scores of each of the sub-templates are posted in a voting area. After all the sub-templates have correlated with the image, this area is then polled for clusters of votes, whose density determines the success of the comparison. It is seen that pattern matching techniques are dependent on the clarity of data they process, and a method for capturing fingerprint images of a consistently high quality is presented. A parallel template matching architecture comprising an array of 32 correlation cells is also presented. The array enables the simultaneous correlation of four sub-templates with eight areas of the image. This architecture makes use of industry standard byte wide random access memories (RAM) for storing the reference templates and the image. The algorithms that comprise the fingerprint comparison system are taken from concepts, through a stage of empirical development and extensive field trials, to an eventual compact and cost effective Very Large Scale Integration (VLSI) Application Specific Integrated Circuit (ASIC) based implementation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:642136 |
| Date | January 1993 |
| Creators | Bruce, William Henry |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/13146 |
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