Spatiotemporal Deep Learning for Behavioral Biometrics

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a behavioral biometrics deep learning (BBDL) pipeline comprising a plurality of stages of machine learning computer models that operate to:

• receive, from one or more sensors, spatiotemporal input data corresponding to an input associated with an entity over a predetermined time window comprising a plurality of time intervals, each time interval having a corresponding subset of the spatiotemporal input data, wherein the spatiotemporal input data comprises input data that is both spatially dependent with regard to a physical area monitored by at least one of the one or more sensors, and time dependent;
• for each time interval in the plurality of time intervals, process, by one or more machine learning computer models of a corresponding stage in the plurality of stages, a subset of the spatiotemporal input data corresponding to the time interval to generate an output vector having values indicative of an internal representation of spatiotemporal traits of the entity represented in the subset of the spatiotemporal input data;
• accumulate output vectors, across the plurality of stages of the BBDL pipeline, to generate a final output vector comprising final output vector values indicative of the spatiotemporal traits of the entity represented in the spatiotemporal input data;
• and
• authenticate, by the data processing system, the entity based on the final output vector.

An apparatus comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement a behavioral biometrics deep learning (BBDL) pipeline comprising a plurality of stages of machine learning computer models that operate to:
• receive, from one or more sensors, spatiotemporal input data corresponding to an input associated with an entity over a predetermined time window comprising a plurality of time intervals, each time interval having a corresponding subset of the spatiotemporal input data, wherein the spatiotemporal input data comprises input data that is both spatially dependent with regard to a physical area monitored by at least one of the one or more sensors, and time dependent;
• for each time interval in the plurality of time intervals, process, by one or more machine learning computer models of a corresponding stage in the plurality of stages, a subset of the spatiotemporal input data corresponding to the time interval to generate an output vector having values indicative of an internal representation of spatiotemporal traits of the entity represented in the subset of the spatiotemporal input data;
• accumulate output vectors, across the plurality of stages of the BBDL pipeline, to generate a final output vector comprising final output vector values indicative of the spatiotemporal traits of the entity represented in the spatiotemporal input data;
• and
• authenticate, by the data processing system, the entity based on the final output vector.