A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

A warning system according to an embodiment includes an acquisition unit, an analysis unit, and a calculation unit. The acquisition unit acquires, from first sensor data including information on a target, condition information on the target. The analysis unit analyzes, from second sensor data including information on a region, current state information on an object included in the region and action prediction information on the object. The calculation unit calculates a degree of caution, based on the condition information on the target, the current state information on the object, and the action prediction information on the object.

A data processing system and a method are provided for recognizing a scanned biometric characteristic in the data processing system. The data processing system includes a biometric sensor, a rich execution environment (REE), and a secure element (SE). In one embodiment, during an enrollment operation, a random challenge is applied to scanned data to produce a biometric template that is stored. During subsequent validation operations, the SE determines if user data includes evidence of the random challenge before providing access to a secure application. Evidence of the random challenge indicates the user data was provided by the biometric sensor. In another embodiment, the sensor data is split between the REE and the SE and partially processed in the SE. The described embodiments prevent a replay attack from being conducted in communications between the REE and the SE.

An apparatus includes a memory and a processor. The memory stores biometric data obtained from an account holder at a first location and identification information provided by the account holder that was used to open an existing account at the first location. The processor receives, from a device, data corresponding to an applicant that includes biometric data obtained from the applicant at a second location and identification information provided by the applicant at the second location. The applicant is requesting to open a new account using the identification information at the second location. In response to receiving the applicant's data, the processor determines that the applicant's biometric data matches the account holder's biometric data. The processor also determines that the applicant's identification information does not match the account holder's identification information. In response, the processor transmits an alert to the device indicating that the applicant's identity could not be verified.

A data processing system and a method are provided for recognizing a scanned biometric characteristic in the data processing system. The data processing system includes a biometric sensor, a rich execution environment (REE), and a secure element (SE). In one embodiment, during an enrollment operation, a random challenge is applied to scanned data to produce a biometric template that is stored. During subsequent validation operations, the SE determines if user data includes evidence of the random challenge before providing access to a secure application. Evidence of the random challenge indicates the user data was provided by the biometric sensor. In another embodiment, the sensor data is split between the REE and the SE and partially processed in the SE. The described embodiments prevent a replay attack from being conducted in communications between the REE and the SE.

A data processing system and a method are provided for recognizing a scanned biometric characteristic in the data processing system. The data processing system includes a biometric sensor, a rich execution environment (REE), and a secure element (SE). In one embodiment, during an enrollment operation, a random challenge is applied to scanned data to produce a biometric template that is stored. During subsequent validation operations, the SE determines if user data includes evidence of the random challenge before providing access to a secure application. Evidence of the random challenge indicates the user data was provided by the biometric sensor. In another embodiment, the sensor data is split between the REE and the SE and partially processed in the SE. The described embodiments prevent a replay attack from being conducted in communications between the REE and the SE.

A data processing system and a method are provided for recognizing a scanned biometric characteristic in the data processing system. The data processing system includes a biometric sensor, a rich execution environment (REE), and a secure element (SE). In one embodiment, during an enrollment operation, a random challenge is applied to scanned data to produce a biometric template that is stored. During subsequent validation operations, the SE determines if user data includes evidence of the random challenge before providing access to a secure application. Evidence of the random challenge indicates the user data was provided by the biometric sensor. In another embodiment, the sensor data is split between the REE and the SE and partially processed in the SE. The described embodiments prevent a replay attack from being conducted in communications between the REE and the SE.

A device and method for achieving a high probability of match for identifying individuals in a particular group, is provided. The device and method consists of two stages. The first stage compares a biometric template against personnel in an authorized group with a high probability of match standard. In the second stage, in response to no match being made in the authorization group, a search would be conducted against a second group, such as a watch list, with the same or lower probability of match rate.

Methods, systems, and computer program products are provided for tracking user actions made via a user account, and to accurately detect fraudulent transactions made therewith. Information associated with the user actions such as, for example, device ID, device IP address, and device IP location, is captured and stored. Stored information is used to create features. The features are assembled into an n-dimensional vector, and a measure similarity between that vector and a previously created n-dimensional vector can be computed. The measure of similarity may be used to assess the probability that the present transaction is fraudulent. Alternatively, one or more n-dimensional vectors, and/or the computed measure of similarity may be used as input to a machine learning model. The output of machine learning model also may be used to assess the probability that the present transaction is fraudulent.

An approach is disclosed that receives a set of visual media data that corresponds to a person. The process detects that a portion of the visual media data is biometric data that corresponds to the person. Responsively, the process alters the biometric data so that the altered biometric data fails to identify the person.