Abstract: Preferably a sensor receives a print image from an authorized person to form a template, and from a candidate to form test data. Noise variance is estimated from the test data as a function of position in the image, and used to weight the importance of comparison with the template at each position. Test data are multilevel, and are bandpassed and normalized—and expressed as local sinusoids—for comparison. A ridge spacing and direction map of the template is stored as vector wavenumber fields, which are later used to refine comparison. Global dilation—and also differential distortions—of the test image are estimated, and taken into account in the comparison. Comparison yields a test statistic that is the ratio, or log of the ratio, of the likelihoods of obtaining the test image assuming that it respectively was, and was not, formed by an authorized user.

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.

Abstract: Preferably a sensor receives a print image from an authorized person to form a template, and from a candidate to form test data. Noise variance is estimated from the test data as a function of position in the image, and used to weight the importance of comparison with the template at each position. Test data are multilevel, and are bandpassed and normalized--and expressed as local sinusoids--for comparison. A ridge spacing and direction map of the template is stored as vector wavenumber fields, which are later used to refine comparison. Global dilation--and also differential distortions--of the test image are estimated, and taken into account in the comparison. Comparison yields a test statistic that is the ratio, or log of the ratio, of the likelihoods of obtaining the test image assuming that it respectively was, and was not, formed by an authorized user.