Abstract: Entity resolution (i.e., record linkage) involves the analysis/discovering of datasets that refer to the same real world entity. Analysis typically involves transformation and comparison of different fields of the dataset followed by the application of often domain/data specific logic for determining datasets that refer to the same real world entity (e.g., person). Consider, a bulk mailing of product catalogs to potential customers. Some individuals may have numerous public records that identify the individual differently. Illustratively, several records associated with Jane Doe at her current home address may exist: one record with her name listed as J. Doe, a second record as Jane H. Doe, a third record as Doe, Jane, and a fourth record as Jan Doe (a misspelling). Conceivably, the bulk mailing could unwittingly send multiple catalogs to Jane Doe at her current address, one for each name variation. The entity resolution process described herein can overcome such problems.

Abstract: Entity resolution (i.e., record linkage) involves the analysis/discovering of datasets that refer to the same real world entity. Analysis typically involves transformation and comparison of different fields of the dataset followed by the application of often domain/data specific logic for determining datasets that refer to the same real world entity (e.g., person). Consider, a bulk mailing of product catalogs to potential customers. Some individuals may have numerous public records that identify the individual differently. Illustratively, several records associated with Jane Doe at her current home address may exist: one record with her name listed as J. Doe, a second record as Jane H. Doe, a third record as Doe, Jane, and a fourth record as Jan Doe (a misspelling). Conceivably, the bulk mailing could unwittingly send multiple catalogs to Jane Doe at her current address, one for each name variation. The entity resolution process described herein can overcome such problems.

Abstract: Systems and methods for repairing cross-linked biometric records receive a set of biometric records. Each biometric record contains at least one biometric sample in a non-textual modality. One or more of the biometric records in the set of biometric records is potentially a cross-linked biometric record having at least two biometric samples that are associated with different individuals. Crosslink resolution is performed on the set of biometric records by searching for a match between a biometric sample in a given non-textual modality of a given biometric record with each biometric sample of the same given non-textual modality in each of the other biometric records in the set of biometric records. During the crosslink resolution, biometric sample may be removed from one biometric record and merged with another biometric record.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: Entity resolution (i.e., record linkage) involves the analysis/discovering of datasets that refer to the same real world entity. Analysis typically involves transformation and comparison of different fields of the dataset followed by the application of often domain/data specific logic for determining datasets that refer to the same real world entity (e.g., person). Consider, a bulk mailing of product catalogs to potential customers. Some individuals may have numerous public records that identify the individual differently. Illustratively, several records associated with Jane Doe at her current home address may exist: one record with her name listed as J. Doe, a second record as Jane H. Doe, a third record as Doe, Jane, and a fourth record as Jan Doe (a misspelling). Conceivably, the bulk mailing could unwittingly send multiple catalogs to Jane Doe at her current address, one for each name variation. The entity resolution process described herein can overcome such problems.

Abstract: Methods and systems for high-speed filtering of candidate fingerprint images in a gallery using a non-reference-point based matching involving histograms of tokens representing minutia data. The method or system may involve detecting minutia patterns in a probe image, defining tokens that represent the minutia patterns detected in the probe image, measuring a degree of similarity between the probe image and each gallery image by comparing a probability of occurrence of a set of the tokens defined for the probe image with a probability of occurrence of the same set of tokens in that gallery image, and identifying as a candidate for a match to the probe image each gallery image for which the measured degree of similarity satisfies a criterion.

Abstract: Systems and methods for repairing cross-linked biometric records receive a set of biometric records. Each biometric record contains at least one biometric sample in a non-textual modality. One or more of the biometric records in the set of biometric records is potentially a cross-linked biometric record having at least two biometric samples that are associated with different individuals. Crosslink resolution is performed on the set of biometric records by searching for a match between a biometric sample in a given non-textual modality of a given biometric record with each biometric sample of the same given non-textual modality in each of the other biometric records in the set of biometric records. During the crosslink resolution, biometric sample may be removed from one biometric record and merged with another biometric record.

Abstract: Entity resolution (i.e., record linkage) involves the analysis/discovering of datasets that refer to the same real world entity. Analysis typically involves transformation and comparison of different fields of the dataset followed by the application of often domain/data specific logic for determining datasets that refer to the same real world entity (e.g., person). Consider, a bulk mailing of product catalogs to potential customers. Some individuals may have numerous public records that identify the individual differently. Illustratively, several records associated with Jane Doe at her current home address may exist: one record with her name listed as J. Doe, a second record as Jane H. Doe, a third record as Doe, Jane, and a fourth record as Jan Doe (a misspelling). Conceivably, the bulk mailing could unwittingly send multiple catalogs to Jane Doe at her current address, one for each name variation. The entity resolution process described herein can overcome such problems.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: Methods and systems for high-speed filtering of candidate fingerprint images in a gallery using a non-reference-point based matching involving histograms of tokens representing minutia data. The method or system may involve detecting minutia patterns in a probe image, defining tokens that represent the minutia patterns detected in the probe image, measuring a degree of similarity between the probe image and each gallery image by comparing a probability of occurrence of a set of the tokens defined for the probe image with a probability of occurrence of the same set of tokens in that gallery image, and identifying as a candidate for a match to the probe image each gallery image for which the measured degree of similarity satisfies a criterion.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: Methods and systems for high-speed filtering of candidate fingerprint images in a gallery using a non-reference-point based matching involving histograms of tokens representing minutia data. The method or system may involve detecting minutia patterns in a probe image, defining tokens that represent the minutia patterns detected in the probe image, measuring a degree of similarity between the probe image and each gallery image by comparing a probability of occurrence of a set of the tokens defined for the probe image with a probability of occurrence of the same set of tokens in that gallery image, and identifying as a candidate for a match to the probe image each gallery image for which the measured degree of similarity satisfies a criterion.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

Abstract: Systems and methods for repairing cross-linked biometric records receive a set of biometric records. Each biometric record contains at least one biometric sample in a non-textual modality. One or more of the biometric records in the set of biometric records is potentially a cross-linked biometric record having at least two biometric samples that are associated with different individuals. Crosslink resolution is performed on the set of biometric records by searching for a match between a biometric sample in a given non-textual modality of a given biometric record with each biometric sample of the same given non-textual modality in each of the other biometric records in the set of biometric records. During the crosslink resolution, biometric sample may be removed from one biometric record and merged with another biometric record.

Abstract: An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.