A method and an associated device for detecting fraud during automatic face recognition, the method comprising the following steps: acquiring a first image of the face by means of a first sensor having a first field angle, and a second image of the face by means of a second sensor having a second field angle that is narrower than the first field angle; analyzing the first image to verify that there is no frame around the face; and analyzing the second image to verify that there is no moiré effect.

A technology is provided for using a multi-factor authentication process to access services in a computing service environment. One or more policies can be defined for allowing access to one or more services and/or resources associated with a service provider environment according to an authenticated identity. A device, detected by a voice-capturing endpoint within a defined geographical location, may be authenticated according to a unique identification (ID). Voice data received from the voice-capturing endpoint can be authenticated. The authenticated identity can be established according to the authenticated device and the authenticated voice data. A command, received via a voice command from the voice-capturing endpoint, may be issued with the authenticated identity to access the one or more services and/or resources associated with the service provider environment according to the plurality of policies.

Biometric identification methods and apparatuses (including devices and systems) for uniquely identifying one an individual based on wearable garments including a plurality of sensors, including but not limited to sensors having multiple sensing modalities (e.g., movement, respiratory movements, heart rate, ECG, EEG, etc.).

A method for operating a two-dimensional touch array by providing virtual grid intersections. The techniques may be used to improve the array resolution. It may also be used to detect fingerprint ridge and valley detail even when the finger is not moving, and when the array node spacing is much greater than the ridge and valley spacing.

A biometric enrollment system can include a processor and a data store that stores one or more TPS templates and a sample population. The processor can be configured to receive a reference template associated with a subject, generate a cohort set based on a plurality of templates from the sample population, and perform a one-to-many comparison process on the reference template and the cohort set, wherein an output of the one-to-many comparison process comprises one or more cohort vectors. The processor can normalize the one or more cohort vectors to obtain one or more normalized cohort vectors, and can apply a lossy transformation to the one or more normalized cohort vectors. An output of applying the lossy transformation can comprise one or more transformed, privacy-secured (TPS) templates that are stored in the database.

A biometric enrollment system can include a processor and a data store that stores one or more TPS templates and a sample population. The processor can be configured to receive a reference template associated with a subject, generate a cohort set based on a plurality of templates from the sample population, and perform a one-to-many comparison process on the reference template and the cohort set, wherein an output of the one-to-many comparison process comprises one or more cohort vectors. The processor can normalize the one or more cohort vectors to obtain one or more normalized cohort vectors, and can apply a lossy transformation to the one or more normalized cohort vectors. An output of applying the lossy transformation can comprise one or more transformed, privacy-secured (TPS) templates that are stored in the database.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying a user in a multi-user environment. One of the methods includes receiving, by a first user device, an audio signal encoding an utterance, obtaining, by the first user device, a first speaker model for a first user of the first user device, obtaining, by the first user device for a second user of a second user device that is co-located with the first user device, a second speaker model for the second user or a second score that indicates a respective likelihood that the utterance was spoken by the second user, and determining, by the first user device, that the utterance was spoken by the first user using (i) the first speaker model and the second speaker model or (ii) the first speaker model and the second score.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying a user in a multi-user environment. One of the methods includes receiving, by a first user device, an audio signal encoding an utterance, obtaining, by the first user device, a first speaker model for a first user of the first user device, obtaining, by the first user device for a second user of a second user device that is co-located with the first user device, a second speaker model for the second user or a second score that indicates a respective likelihood that the utterance was spoken by the second user, and determining, by the first user device, that the utterance was spoken by the first user using (i) the first speaker model and the second speaker model or (ii) the first speaker model and the second score.

Methods and systems for verifying a user's identity on a computing device using two-factor authentication are described. More particularly, the system can use a personal identification number input by a user, together with one or more of a secure browsing feature, a device fingerprint, and a token generator to authenticate the user on the computer.

Methods and systems for verifying a user's identity on a computing device using two-factor authentication are described. More particularly, the system can use a personal identification number input by a user, together with one or more of a secure browsing feature, a device fingerprint, and a token generator to authenticate the user on the computer.