Computerized systems and methods for determining authenticity using micro expressions

A computing system for calculating authenticity of a human user, comprising:

• at least one processor;
• at least one memory storing instructions, wherein the instructions cause the at least one processor to:
• receive, via a network, a request, the request instantiating a connection with a user device;
• provide, via the network, a first question to the user device;
• extract a plurality of facial expressions from data received via the connection;
• calculate, using a first convolutional neural network, first data corresponding to one or more predetermined emotions based on at least one extracted facial expression, and using a second convolutional neural network, second data corresponding to the one or more predetermined emotions based on at least two extracted facial expressions and the received data;
• generate candidate emotion data using the first and second data;
• determine whether the candidate emotion data predicts a predetermined emotion;
• and
• based on determining whether the candidate emotion data predicts the predetermined emotion:
• generate a second question to collect additional data for aggregating with the first and second data;
• or
• determine the authenticity of the human user and using the determined authenticity to decide on the request.

A method for determining authenticity of a loan applicant, performed by a user device having at least one processor, the method comprising:

• receiving, via a network, a request, the request instantiating a connection with a user device;
• providing, via the network, a first question to the user device;
• extracting a plurality of facial expressions from data received via the connection;
• calculating, using a first convolutional neural network, first data corresponding to one or more predetermined emotions based on at least one extracted facial expression, and using a second convolutional neural network, second data corresponding to the one or more predetermined emotions based on at least two extracted facial expressions and the received data;
• generating candidate emotion data using the first and second data;
• determining whether the candidate emotion data predicts a predetermined emotion;
• and
• based on determining whether the candidate emotion data predicts the predetermined emotion:
• generating a second question to collect additional data for aggregating with the first and second data;
• or
• determining the authenticity of the human user and using the determined authenticity to decide on the request.