US 12,002,295 B2
System and method for video authentication
Ankit Suneja, Pune (IN); Rajeev Divakaran Nair, Bangalore (IN); and S. Abishek Kumar, Bangalore (IN)
Assigned to ACCENTURE GLOBAL SOLUTIONS LIMITED, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Aug. 13, 2021, as Appl. No. 17/401,609.
Prior Publication US 2023/0058259 A1, Feb. 23, 2023
Int. Cl. G06V 40/40 (2022.01); G06F 18/25 (2023.01); G06N 20/00 (2019.01); G06V 40/16 (2022.01); G06V 40/70 (2022.01); G10L 17/06 (2013.01)
CPC G06V 40/45 (2022.01) [G06F 18/253 (2023.01); G06N 20/00 (2019.01); G06V 40/172 (2022.01); G06V 40/70 (2022.01); G10L 17/06 (2013.01); G06V 40/178 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A computer implemented method of applying machine learning to authenticate a customer's identity via live video, comprising:
building convolutional neural network (“CNN”) models for at least two of a sentiment module, a face identity document match (“face ID match”), a liveness module, a voice module, and a politically exposed person (“PEP”) module on at least two different computation nodes by:
receiving, at a model building planner, module data;
creating, by the model building planner, dispatchable transaction packages at a module data building pool;
sharing the dispatchable transaction packages with the at least two computation nodes; and
dynamically coordinating, by a model building scheduler, training progress among the at least two computation nodes;
receiving captured live video comprising a person's voice and images including a person's face and an image of a photo identity document (“photo ID”);
processing the images through the sentiment module to generate a sentiment score based on the person's face as it appears in the live video;
processing the images through the face ID match module to generate a face ID score based on the person's face as it appears in the live video;
processing the images through the liveness module to generate a liveness score based on the person's face as it appears in the live video;
processing the person's voice through the voice module to generate a voice score based on the person's voice as it sounds in the live video;
processing the images and the person's voice through the PEP module to generate a PEP score based on the person's face and the person's voice; and
in response to determining that one or both of the face ID score and the liveness score is “fail,” determining whether a condition offsets the failing face ID score and/or the failing liveness score;
processing the images through a machine learning model to determine, based on the photo ID, whether the person's age is above a predetermined threshold, wherein the person's age is determined to be above the predetermined threshold and the person's age is the condition; and
in response to determining that PEP score is “pass,” disallowing an offset based on whether a condition offsets the score of “fail.”