Abstract
An all-digital ring-wedge detector system is presented that simulates the analog multielement array commonly used in coherent optoelectronic processors. The system is applicable with either hard-copy or digital imagery. Using neural-network software, we demonstrate high accuracy for the recognition of fingerprints, including both orientation and wide-scale size-independent sortings by using ring-only and wedge-only input neurons, respectively. Also, the system is applied on windowed subregions of fingerprint imagery, providing a feature set that summarizes localized information about spatial-frequency content and edge-angle correlations. Examples are presented in which this localized spatial-frequency information is used to produce local ridge-orientation maps and to detect regions of poor print quality. In summary, both direct-image data and spatial-transform data are found to be important.
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