Embodiments may generally be directed components and techniques to detect a request to provide banking account information over a one or more voice connections, identify the requested banking account information, and generate speech data representing the banking account information requested. In embodiments further include communicating the speech data to another device.

This disclosure describes techniques for parallel fingerprint capturing and matching, thereby enabling large-area or high-resolution fingerprint identification with low latency. Rather than waiting to capture an entire fingerprint image (“a verify image”), a fingerprint identification process divides the verify image into blocks and attempts to match the blocks to corresponding portions of an enrolled image even as other portions are being. Rather than waiting to capture and analyze the entire fingerprint image at once, small groups of blocks are captured and the already-captured blocks are matched and scored to corresponding blocks of an enrolled image, in some cases, while additional blocks of the verify image are being captured. A cumulative score and cumulative confidence in the overall matching of the enrolled image is derived from the scores and confidences of the individual block scores and the verify image is authenticated based on each satisfying their respective thresholds.

This disclosure describes techniques for parallel fingerprint capturing and matching, thereby enabling large-area or high-resolution fingerprint identification with low latency. Rather than waiting to capture an entire fingerprint image (“a verify image”), a fingerprint identification process divides the verify image into blocks and attempts to match the blocks to corresponding portions of an enrolled image even as other portions are being. Rather than waiting to capture and analyze the entire fingerprint image at once, small groups of blocks are captured and the already-captured blocks are matched and scored to corresponding blocks of an enrolled image, in some cases, while additional blocks of the verify image are being captured. A cumulative score and cumulative confidence in the overall matching of the enrolled image is derived from the scores and confidences of the individual block scores and the verify image is authenticated based on each satisfying their respective thresholds.

An example operation includes one or more of encrypting, by a transport, received data based on a first biometric associated with an occupant, unencrypting, by the transport, the encrypted data, based on a verification of a second biometric, wherein the second biometric is a continuum of the first biometric, and providing, by the transport, the unencrypted data to the occupant.

An example operation includes one or more of encrypting, by a transport, received data based on a first biometric associated with an occupant, unencrypting, by the transport, the encrypted data, based on a verification of a second biometric, wherein the second biometric is a continuum of the first biometric, and providing, by the transport, the unencrypted data to the occupant.

Computer-implemented detection of anomalous telephone calls, for example detection of interconnect bypass fraud, is disclosed. A telephone call associated with user devices is analyzed remote from the user devices. A first set of multiple features, for example Mel Frequency Cepstral Coefficients, is derived from a call audio stream. The first set is converted to an embedding vector, for example via a model based on a Universal Background Model comprising a Gaussian Mixture Model, which model is preferably configured based on a training plurality of first sets of multiple features derived form a corresponding training plurality of audio streams. Occurrence, or probability of occurrence, of an anomalous telephone call is determined based on the embedding vector, for example via a back-end classifier, such as a Gaussian Backend Model, which classifier is preferably configured based on labels associated with the training plurality of audio streams.

The present disclosure relates to a system for providing customized cosmetics by using genetic information, the system comprising: a terminal that receives user information related to a user; a genetic/medical information server that stores genetic information related to the user; a skin meter that acquires skin information on the user; and a cosmetic manufacturing system that manufactures cosmetics by discharging at least one cosmetic material, wherein the cosmetic manufacturing system can select a cosmetic material to be discharged on the basis of at least one among the user information, the genetic information, and the skin information.

In an aspect, a fingerprint sensing module configured to be integrated in a device for biometric authentication of a user of the device is provided. The fingerprint sensing module comprises a fingerprint sensor and a display configured to display information to the user; the display comprising pixel elements being arranged in the display such that the pixel elements do not obscure a sensing area of the fingerprint sensor in which area the fingerprint sensor is being configured to detect a finger of the user.

In an aspect, a fingerprint sensing module configured to be integrated in a device for biometric authentication of a user of the device is provided. The fingerprint sensing module comprises a fingerprint sensor and a display configured to display information to the user; the display comprising pixel elements being arranged in the display such that the pixel elements do not obscure a sensing area of the fingerprint sensor in which area the fingerprint sensor is being configured to detect a finger of the user.

In an example implementation according to aspects of the present disclosure, a system receives behavioral biometric data associated with a user of a computing device. The system determines whether an identity of the user is authenticated based on a comparison of the received behavior biometric data and historical behavioral biometric data associated with the user of the computing device. In response to determining that the identity of the user is not authenticated based on the comparison of the received behavioral biometric data and the historical behavioral biometric data, the system receives physiological biometric data associated with the user. The system determines whether the identity of the user is authenticated based on a comparison of the received physiological biometric data and historical physiological biometric data associated with the user of the computing device.