Advances in integrated circuit technology have made possible the
application of LSI and VLSI techniques to a wide range of computational
problems. Image processing is one of the areas that stands to benefit most
from these techniques. This thesis presents an architecture suitable for
VLSI implementations which enables a wide range of image processing
operations to be done in a real-time, pipelined fashion. These operations
include filtering, thresholding, thinning and feature extraction.
The particular class of images chosen for study are fingerprints. There
exists a long history of fingerprint classification and comparison techniques
used by humans, but previous attempts at automation have met with little
success. This thesis makes use of VLSI image processing operations to
create a graph structure representation (minutia graph) of the inter-relationships of various low-level features of fingerprint images. An
approach is then presented which allows derivation of a metric for the
similarity of these graphs and of the fingerprints which they represent. An
efficient algorithm for derivation of maximal common subgraphs of two
minutia graphs serves as the basis for computation of this metric, and is
itself based upon a specialized clique-finding algorithm. Results of cross
comparison of fingerprints from multiple individuals are presented
