Aware, Inc.

David Benini

Taras P Riopka

Limin Ma

Robert Mungovan

Louis Scott Hills

Dalys Sebastian

In real biometric systems, false match rates and false non-match rates of 0% do not exist. There is always some probability that a purported match is false, and that a genuine match is not identified. The performance of biometric systems is often expressed in part in terms of their false match rate and false non-match rate, with the equal error rate being when the two are equal. There is a tradeoff between the FMR and FNMR in biometric systems which can be adjusted by changing a matching threshold. This matching threshold can be automatically, dynamically and/or user adjusted so that a biometric system of interest can achieve a desired FMR and FNMR.

Biometric identification and verification

Systems and methods for enhancing confidence in a biometric search result include submitting one or more biometric samples to a biometric search engine. In response to the one or more submitted biometric samples, a plurality of candidates identified as potentially associated with the one or more submitted biometric samples is received from the biometric search engine. Each identified candidate has associated biographic information. The biographic information associated with each identified candidate is submitted to a relationship detection engine. In response to the submitted biographic information, an identified relationship between at least one of the identified candidates and one or more other individuals is received from the relationship detection engine.

Relationship detection within biometric match results candidates

A blockchain database employs cryptography and other methods to implement and protect a distributed, publicly-amendable ledger. Transactions in a blockchain ledger are intentionally anonymous; however, there are cases where it would be useful to be able to verify or disprove a claim of identity of a contributor of a blockchain transaction. Biometrics can be used to link a human being to digital information using their unique physical traits in a way that is analogous to a handwritten or digital signature. An exemplary embodiment disclosed herein describes methods to create and store data in a blockchain transaction such that it can be used in the future to biometrically verify the identity of the contributor of the transaction, and use encoded biometric data to determine whether the blockchain transaction was created or not created by a particular individual.

Biometric verification of a blockchain database transaction contributor

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

A decentralized biometric identity authentication method utilizes biometrics captured on a mobile device to perform identity authentication against data that was registered as part of an identity proofing process and is thus trusted. The user registers his or her biometric using the user's mobile device and associates it with the user's electronic identity as part of a supervised identity proofing process, thus forming a proofed identity, and registers the proofed identity with a federated identity system. To later access the resources of the federated identity system, the user logs in with his or her biometrics. The methods described herein are useful, for example, in the travel, healthcare, and financial services fields.

Decentralized biometric identity authentication

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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Charlestown, MA, United States of America

David Benini