US 12,411,924 B2
Systems and methods for biometric processing with liveness
Scott Edward Streit, Woodbine, MD (US)
Assigned to Private Identity LLC, Potomac, MD (US)
Filed by Private Identity LLC, Potomac, MD (US)
Filed on Aug. 3, 2023, as Appl. No. 18/364,617.
Application 18/364,617 is a continuation of application No. 17/560,813, filed on Dec. 23, 2021, granted, now 11,762,967.
Application 17/560,813 is a continuation of application No. 16/218,139, filed on Dec. 12, 2018, granted, now 11,210,375.
Application 16/218,139 is a continuation in part of application No. 15/914,562, filed on Mar. 7, 2018, granted, now 11,392,802.
Application 16/218,139 is a continuation in part of application No. 15/914,942, filed on Mar. 7, 2018, granted, now 10,721,070.
Application 16/218,139 is a continuation in part of application No. 15/914,969, filed on Mar. 7, 2018, granted, now 11,138,333.
Prior Publication US 2024/0211565 A1, Jun. 27, 2024
Int. Cl. G06F 21/32 (2013.01); G06N 3/08 (2023.01); G06V 40/40 (2022.01)
CPC G06F 21/32 (2013.01) [G06N 3/08 (2013.01); G06V 40/45 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system for evaluating privacy-enabled biometrics for identification or authentication, the system comprising:
at least one processor operatively connected to a memory, the at least one processor when executing is configured to:
process an input including one or more plaintext instance of a first biometric data type, the input including the one or more plaintext instance of the first biometric data type and at least a candidate set of plaintext instances of another biometric data type capable of identification of an entity;
analyze a liveness threshold, wherein analyze the liveness threshold includes operations to determine that the candidate set of plaintext instances matches a random set of instances communicated to the entity to be identified or authenticated;
instantiate at least a first neural network trained to generate one way homomorphic encrypted feature vectors from the one or more plaintext instance of the first biometric type;
accept as an input to the first neural network at least the one or more plaintext instance of the first biometric type to output the one way homomorphic encrypted feature vectors of the first biometric type, and
generate a match to an identity based on the one way homomorphic encrypted feature vectors to one of a plurality of identification classes defined by the one way homomorphic encrypted feature vectors of the first biometric data type, or return an unknown result on a failure to match; and
confirm the match based, at least in part, on meeting the liveness threshold.