Systems and methods for private authentication with helper networks
Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and/or construct authentication models (e.g., embedding and/or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and/or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and/or validation.
1. A system for managing privacy-enabled identification or authentication, the system comprising:
at least one processor operatively connected to a memory;
an identification data gateway, executed by the at least one processor, configured to filter invalid identification information from subsequent verification, enrollment, identification, or authentication functions, the identification data gateway comprising at least:
a first pre-trained validation helper network associated with identification information of a first type, wherein the first pre-trained validation helper network comprises a pre-trained neural network configured to:
evaluate an identification instance of the first type captured on a subject to determine if the identification instance is suitable for use, responsive to input of the identification instance of the first type to the first pre-trained validation helper network, wherein the first pre-trained validation helper network is pre-trained on evaluation criteria that is independent of identification of the subject of the identification instance seeking to be enrolled, identified, or authenticated:
responsive to a determination that the identification instance meets the evaluation criteria, validate the identification instance for use in subsequent verification, enrollment, identification, or authentication that establish the identity of the subject;
responsive to a determination that the identification instance fails the evaluation criteria, reject the information instance for use in subsequent verification, enrollment, identification, or authentication that establish the identity of the subject; and
generate at least a binary evaluation of the identification information instance based on the determination of the evaluation criteria, wherein the at least the binary evaluation includes generation of an output probability by the first pre-trained validation helper network that the identification instance is a valid or an invalid identification information instance;
wherein the authentication data gateway further comprises a plurality of validation helper networks associated with a respective type of identification information including the first pre-trained validation helper network, wherein the plurality of validation helper networks generate at least a binary evaluation of respective identification information inputs, and are configured to validate respective identification information independent of the subject seeking to be enrolled, identified, or authenticated; and include
a first voice helper network trained to validate respective voice identification information independent of the subject seeking to be enrolled, identified, or authenticated; and
a first image helper network trained to validate respective image identification information independent of the subject seeking to be enrolled, identified, or authenticated.
2. The system of claim 1 , wherein the identification data gateway is configured to filter bad audio data from use in subsequent processing.
3. The system of claim 2 , wherein the identification data gateway is configured to accept audio data input and validate the audio input for use in transcription.
4. The system of claim 1 , wherein the first pre-trained validation helper network is trained on presence data, and configured to determine the presence of a target to be evaluated.
5. The system of claim 3 , wherein the first pre-trained validation helper network is configured to validate the presence data independent of the subject seeking to be enrolled, identified, or authenticated.
6. The system of claim 1 , wherein the first pre-trained validation helper network is configured process an image as identification information, and output a probability that the subject is wearing a mask.
7. The system of claim 6 , wherein the first pre-trained validation helper network is configured to determine position of the mask being worn by the subject.
8. The system of claim 6 , wherein the first pre-trained validation helper network is configured to determine the positioning of the mask being worn by the subject irrespective of the subject to be identified.
9. The system of claim 1 , wherein the first pre-trained validation helper network is configured to process location associated input as identification information, and output a probability that the location associated input is invalid.
10. A computer implemented method for managing privacy-enabled identification or authentication, the method comprising:
filtering, by at least one processor, invalid identification information from subsequent verification, enrollment, identification, or authentication functions, wherein the act of filtering includes:
executing, by the at least one processor, a first pre-trained validation helper network associated with identification information of a first type, comprising a pre-trained neural network;
evaluating, by the first pre-trained validation helper network, an identification instance of the first type captured on a subject to determine if the identification instance is suitable for use, responsive to input of the identification instance of the first type to the first pre-trained validation helper network, wherein the first pre-trained validation helper network is pre-trained on evaluation criteria that is independent of identification of the a subject of the identification instance seeking to be verified, enrolled, identified, or authenticated;
validating, by the at least one processor, the identification instance for use in subsequent verification, enrollment, identification, or authentication, in response to determining that the identification instance meets the evaluation criteria that establish the identity of the subject;
rejecting, by the at least one processor, the information instance for use in subsequent verification, enrollment, identification, or authentication responsive to determining that the identification instance fails the evaluation criteria that establish the identity of the subject; and
generating, by the at least one processor, at least a binary evaluation of the identification instance based on the determination of the evaluation criteria, wherein the at least the binary evaluation includes generation of an output probability by the first pre-trained validation helper network that the identification instance is a valid or an invalid identification information instance;
wherein the method further comprises:
executing a plurality of validation helper networks associated with a respective type of identification information including the first pre-trained validation helper network, wherein the plurality of validation helper networks generates at least a binary evaluation of respective identification information inputs to establish validity, and the act of executing the plurality of validation helper network includes:
executing a first voice helper network trained to validate respective voice identification information independent of the subject seeking to be enrolled, identified, or authenticated and a first image helper network trained to validate respective image identification information independent of the subject seeking to be enrolled, identified, or authenticated; and
validating respective identification information independent of the subject seeking to be verified, enrolled, identified, or authenticated.
11. The method of claim 10 , wherein the act of filtering includes an act of filtering bad audio data from use in subsequent processing.
12. The method of claim 11 , wherein the method further comprises accepting audio data input and validating the audio input for use in transcription.
13. The method of claim 10 , wherein the first pre-trained validation helper network is trained on presence data, and the method further comprises determining the presence of a valid target to be evaluated.
14. The method of claim 13 , wherein the method further comprises validating the presence data independent of the subject seeking to be verified, enrolled, identified, or authenticated.
15. The method of claim 10 , wherein the first pre-trained validation helper network is configured process an image as identification information, and the method further comprises an act of outputting a probability that the subject is wearing a mask.
16. The method of claim 15 , wherein the method further comprises determining by the first pre-trained validation helper network that the mask is being worn properly by the subject.
17. The method of claim 15 , wherein the method further comprises determining by the first pre-trained validation helper network that the mask is being worn properly by the subject irrespective of the subject to be identified.
18. The method of claim 10 , wherein method further comprises processing a location associated input as identification information by the first pre-trained validation helper network and generating by the first pre-trained validation helper network a probability that the location associated input is invalid.