Aware, Inc.

Taras P Riopka

Limin Ma

David Benini

Robert Mungovan

Louis Scott Hills

Olukayode Isaac Osesina

Pranab Mohanty

Fang Hua

Siddharth K Madan

Mohamed Lazzouni

Bob Eckel

Jens Peter Hube

Methods and systems are provided that allow a user to access a cryptosystem configured to revoke or cancel biometric credentials associated with the user. The cryptosystem may use randomized seeds and projections to identify elements unique to the user and generate a cancellable cryptographic key set used to verify the identity of the user.

Biometric credentials revocation or cancellable biometrics

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.

System and method for automatically detecting and repairing biometric crosslinks

Face recognition systems are vulnerable to the presentation of spoofed faces, which may be presented to face recognition systems, for example, by an unauthorized user seeking to gain access to a protected resource. A face liveness detection method that addresses this vulnerability utilizes motion analysis to compare the relative movement among three regions of interest in a facial image and based upon that comparison to make a face liveness determination.

Face liveness detection using background/foreground motion analysis

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.

Automatic entity resolution with rules detection and generation system

Spoofed faces are unlikely to reflect a liveness detection image in the same manner as live faces. To determine liveness of a subject face, a liveness detection image is displayed on a screen of a face recognition device in the direction of the subject face, and a reflection image of the subject face is captured while the liveness detection image is displayed. The reflection image is analyzed to determine whether the reflection image contains any reflections of the liveness detection image, and a liveness determination is made based on the location and/or character of any such reflections.

Analysis of reflections of projected light in varying colors, brightness, patterns, and sequences for liveness detection in biometric systems

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. Thus, the methodology eliminates false identifications of specific instances of biometric features while accentuating true identifications.

Biometric feature extraction using multiple image instantiations

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.

High speed reference point independent database filtering for fingerprint identification

Face recognition systems are vulnerable to the presentation and spoofed faces, which may be presented to face recognition systems, for example, by an unauthorized user seeking to gain access to a protected resource. A face liveness detection method that addresses this vulnerability utilizes motion analysis to compare the relative movement among three regions of interest in a facial image, and based upon that comparison to make a face liveness determination.

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.

System and method of capturing and producing biometric-matching quality fingerprints and other types of dactylographic images with a mobile device

Spoofed faces are unlikely to reflect a likeness detection image in the same manner as live faces. To determine likeness of a subject face, a likeness detection image is displayed on a screen of a face recognition device in the direction of the subject face, and a reflection image of the subject face is captured while the likeness detection image is displayed. The reflection image is analyzed to determine whether the reflection image contains any reflections of the likeness detection image, and a likeness determination is made based on the location and/or character of any such reflections.

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Concord, MA, United States of America

Taras P Riopka