Abstract: A system is disclosed for identifying a user based on the classification of characteristic data collected for that user. An identity verification system accesses characteristic data collected by a sensor for a plurality of users. When a user requests access to an operational context, the identity verification system inputs characteristic data collected for the user to an identity confidence model to generate an authentication result, indicating whether the user was granted access to the operational context. The identity verification system updates a false acceptance rate and a false rejection rate for the identity confidence model based the authentication result. Because the false acceptance rate and the false rejection rate for the identity confidence model characterize the performance of the identity confidence model, the identity verification system adjusts one or more parameters of the identity confidence model to improve reduce the false acceptance rate and the false rejection rate.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data characterizing movements performed by a target user. The sequence of motion data is received as a point cloud of the motion data. The point cloud is input a machine-learned model trained based on manually labeled point clusters of a training set of motion data that each represent a movement. The machine-learned model identifies a movement represented by the point cloud of the motion data and assigns a label describing the movement the point cloud. The system generates a labeled representation of the sequence of motion data comprising the label identifying a portion of the sequence of motion data corresponding to the identified movement.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.

Abstract: A system and method are disclosed for identifying a user based on the classification of user movement data. An identity verification system receives a sequence of motion data from a mobile device operated by a target user. From the sequences of motion data, the identity verification system identifies a plurality of identity blocks representing different movements performed by the target user and encodes a set of signature sequences from each identity block into a feature vector. Each feature vector is input to a confidence model to output an identity confidence value for an identity block. An identity confidence value describes a confidence that the movement in the identity block was performed by the target user. The identity confidence value is compared to an operational security threshold and if identity confidence value is above the threshold, the target user is granted access to an operational context.