Abstract: An apparatus adapted to identify a user based on keystroke dynamics of an input by the user, the apparatus adapted to: execute a first training phase of training a keystroke sample generator to generate negative keystroke samples; execute a second training phase of training a user identification model based at least in part on a plurality of negative keystroke samples generated using the keystroke sample generator; and execute a deployment of the user identification model to authenticate an input sample associated with the user using the trained user identification model.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for partitioning a captured motion signal of a user into segments. One or more sets of features are extracted from the segmented motion signal by applying a plurality of feature extraction algorithms to the segments, where the feature extraction algorithms include Convolutional Vision Transformers (CVT) and convolutional gated recurrent units (convGRU) model algorithms. A subset of discriminative features are selected from the sets of extracted features. The subset of features is then analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.

Abstract: A system and method for determining whether persons depicted in images are wearing personal protective equipment (PPE) is disclosed. The system includes a PPE detection platform that, during a testing phase, comprises a person detector configured to detect persons in images and a deep neural network PPE classifier. The PPE classifier comprises a spatial attention module and is trained to predict whether or not a PPE item is present in the image sub-region corresponding to the detected person. During a training phase, the PPE detection platform comprises a body pose estimator that, based on the output of the person detector, determines a pose of the detected person's body and provides a spatial attention supervision signal used to train the spatial attention module of the PPE classifier and tune the PPE classifier. The PPE platform is configured to bypass the body pose estimator during the inference phase.

Abstract: A system and method for determining whether persons depicted in images are wearing personal protective equipment (PPE) is disclosed. The system includes a PPE detection platform that, during a testing phase, comprises a person detector configured to detect persons in images and a deep neural network PPE classifier. The PPE classifier comprises a spatial attention module and is trained to predict whether or not a PPE item is present in the image sub-region corresponding to the detected person. During a training phase, the PPE detection platform comprises a body pose estimator that, based on the output of the person detector, determines a pose of the detected person's body and provides a spatial attention supervision signal used to train the spatial attention module of the PPE classifier and tune the PPE classifier. The PPE platform is configured to bypass the body pose estimator during the inference phase.

Abstract: Methods and systems for disentangling discriminative features of a user of a device from motion signals and authenticating a user on a mobile device are provided. In at least one aspect of the methods and systems, each captured motion signal is divided into segments. The segments are then converted into translated segments using one or more trained translation algorithms. The segments and translated segments are then provided to a machine learning system. Discriminative features of the user are then extracted from the segments and translated segments with the processor using the machine learning system that applies one or more feature extraction algorithms.

Abstract: Technologies are presented herein in support of system and methods for user recognition using motion sensor data. Embodiments of the present invention concern a system and method for capturing motion sensor data using motion sensors of a mobile device and characterizing the motion sensor data into features for user recognition. The motion sensor data of a user is collected by the motion sensors of a mobile device in the form of a motion signal. One or more sets of features are extracted from the motion signal and a subset of discriminative features are then selected. The subset of features is analyzed, and a classification score is generated to classify the user as a genuine user or an imposter user.