Abstract: Preferably a sensor receives a print image from an authorized person to form a template, and from a candidate to form test data. Power spectral density (PSD) data for the template and candidate are compared, to read out rotation & dilation; these are used to adjust the template or candidate preparatory to a correlation to find translation. After applying the translation, and refinement of the rotation and dilation, normalized spatial correlation values (NSCVs) are used as a measure of quality of the match—and thresholded to make an early rejection or acceptance decision in very clear cases. Where the question is closer, isomorphic adjustment is applied to the entire template or candidate for a fairer comparison in their overlap area. Such comparison proceeds by the same type of PSD analysis—but for multiple subregions in the overlap area. Resulting NSCVs are averaged to obtain a measure of quality of the match, which again is thresholded for a final decision in the closer cases.

Abstract: Preferably a sensor receives a print image from an authorized person to form a template, and from a candidate to form test data. Power spectral density (PSD) data for the template and candidate are compared, to read out rotation & dilation; these are used to adjust the template or candidate preparatory to a correlation to find translation. After applying the translation, and refinement of the rotation and dilation, normalized spatial correlation values (NSCVs) are used as a measure of quality of the match—and thresholded to make an early rejection or acceptance decision in very clear cases. Where the question is closer, isomorphic adjustment is applied to the entire template or candidate for a fairer comparison in their overlap area. Such comparison proceeds by the same type of PSD analysis—but for multiple subregions in the overlap area. Resulting NSCVs are averaged to obtain a measure of quality of the match, which again is thresholded for a final decision in the closer cases.