CPC H04L 9/3231 (2013.01) [G06F 21/32 (2013.01); G06F 21/6245 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/194 (2022.01); G06V 40/172 (2022.01); H04L 9/008 (2013.01)] | 21 Claims |
1. A privacy-enabled biometric system comprising:
at least one processor operatively connected to a memory, the at least one processor configured to:
execute a first machine learning (“ML”) process based on an authentication mode, wherein the first ML process when executed by the at least one processor is configured to accept distance measurable encrypted feature vectors as input and classify the distance measurable encrypted feature vector input as part of identification or authentication of an entity using a first classification neural network trained on the distance measurable encrypted feature vectors for a plurality of identification classes for an identification data type, to determine a match to information of the identification data type;
execute a second ML process based on the authentication mode, wherein the second ML process when executed by the at least one processor is configured to:
process plain text identification inputs of the identification data type using a pre-trained generation neural network trained to generate distance measurable encrypted feature vectors from the plain text identification of the identification data type; and
compare distances between at least one stored distance measurable encrypted feature vector and a newly generated distance measurable encrypted feature vector of the identification data type during identification or authentication of an entity to determine a match; and
return a label associated with the entity identified by one or both of the first ML process or the second ML process, or return an unknown result on failure to match.
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