Biometric behavior-based
authentication may be enhanced by using convolutional
deep neural networks to learn subject-specific features for each subject. The
advantage is two-fold. First the need for
a domain expert is eliminated, and the search space can be algorithmically explored. Second, the features that allow each subject to be differentiated from other subjects may be used. This allows the
algorithm to learn the aspects of each subject that make them unique, rather than taking a set of fixed aspects and learning how those aspects are differentiated across subjects. The
combined result is a far more effective
authentication in terms of reduction of errors. Behavior-based, invisible multi-factor
authentication (BIMFA) mays also automate the responses to authentication second and third factor requests (something you have and something you are). BIMFA leverages continuous, invisible behavioral
biometrics on user devices to
gain a continuous estimate of the
authorization state of the user across multiple devices without requiring any explicit user interaction or input for authentication. As a result, BIMFA can demonstrate that a device is under the control of the authorized user without requiring any direct user interaction.