Abstract: The disclosed embodiments provide a system that identifies a user of an electronic device. During a training mode, the system uses an initial training data set, comprising sensor data from electronic devices associated with a set of initial users, to train a multilayer neural network model to authenticate the initial users. Next, the system uses an additional training data set, which includes sensor data from electronic devices associated with one or more new users, to update a portion of the weights in the trained model so that the updated model can be used to authenticate both the initial users and the one or more new users. During a subsequent mode, the system uses the updated model to authenticate a user of the electronic device based on sensor data contemporaneously received from the electronic device.

Abstract: The disclosed embodiments provide a system that identifies a user of an electronic device. During a training mode, the system uses an initial training data set, comprising sensor data from electronic devices associated with a set of initial users, to train a multilayer neural network model to authenticate the initial users. Next, the system uses an additional training data set, which includes sensor data from electronic devices associated with one or more new users, to update a portion of the weights in the trained model so that the updated model can be used to authenticate both the initial users and the one or more new users. During a subsequent surveillance mode, the system uses the updated model to authenticate a user of the electronic device based on sensor data contemporaneously received from the electronic device.