Secure identification methods and systems

1 . A method for generating a unique algebraic lattice identifier for a subject, the method executable by a processor of a computer system, the method comprising:

obtaining biometric data relating to the subject from a vibroacoustic sensor having a bandwidth ranging from 0.01 Hz to 160 kHz;

extracting an identification marker from the biometric data comprising at least one of cross-frequency coupling and cross-phase coupling from the biometric data; and

generating the unique algebraic lattice identifier from the extracted identification marker, wherein the generating the unique identifier comprises identifying a given domain specific feature having a predetermined identity compared to other domain specific features.

2 . The method of claim 1 , wherein the predetermined identity has a higher occurrence than other domain specific features, or comprises a higher frequency event than other domain specific features.

3 . The method of claim 1 , wherein the given domain specific feature is characterized based on one or more of a pitch, an amplitude, a peak, an area under the curve, a frequency, a spectral distribution, a total energy, a timbre, a timing, frequency bands, and harmonic distribution.

4 . The method of claim 1 , further comprising storing the generated unique identifier in a database of the processor.

5 . The method of claim 1 , wherein the generating the unique algebraic lattice identifier occurs at a first time point, the method further comprising, at a second time point which is later than or at the same time as the first time point, comparing the generated identifier with a reference identifier to determine a presence of predetermined common features.

6 . The method of claim 1 , further comprising one or more of:

determining whether to accept the biometric data based on predetermined criteria;

processing the biometric data to enhance a signal thereof;

processing the biometric data to normalize a signal thereof, or processing the biometric data to perform evolutionary structural machine learning, signal aggregation and abstraction of a signal thereof.

7 . The method of claim 1 , wherein the obtaining the biometric data comprises a baseline phase and a base-line update phase, a data segment in the baseline phase being larger than a data segment in the base-line update phase.

8 . The method of claim 1 , wherein the obtaining the biometric data comprises acquiring data at sampling rates of about 0.01 Hz to about 20 THz.

9 . The method of claim 1 , further comprising adding a time stamp to the generated unique identifier.

10 . The method of claim 1 , further comprising obtaining input of an event related parameter and generating a status identifier incorporating information about the unique identifier and the event.

11 . The method of claim 1 , further comprising converting a bit output of the generated unique algebraic lattice identifier according to a bit requirement of another digital identification solution which has a different requirement.

12 . The method of claim 1 , further comprising obtaining input of an emotional/cognitive state of the subject, or a health state of the subject and incorporating with the generated unique algebraic lattice identifier.

13 . The method of claim 1 , further comprising use of the generated unique algebraic lattice identifier as one or more of a health state confirmation; emotional/cognitive feedback.

14 . The method of claim 1 , further comprising:

retrieving a profile corresponding to the subject;

obtaining current biometric data of the subject;

retrieving an ordered list of features;

generating, based on the biometric data, one or more feature values corresponding to highest-ranked features in the ordered list of features;

determining a user profile from a database of user profiles that matches the one or more feature values; and

outputting an indication that the subject has been authenticated after determining that the user profile from the database matches the retrieved profile corresponding to the subject.

15 . The method of claim 14 , wherein the ordered list of features was generated by a machine learning algorithm (MLA) trained to rank features based on their ability to discriminate between individuals, wherein features that are more likely to discriminate between individuals are assigned a higher ranking in the ordered list of features.

16 . The method of claim 14 , wherein determining the user profile that matches the one or more feature values comprises:

iterating through the ordered list of features until a unique profile matching the biometric data of the subject is found, or iterating through the ordered list of features until an amount of potential matches for the biometric data of the subject is less than a predetermined threshold amount.

17 . A system for generating a unique algebraic lattice identifier for a subject, the system comprising a processor of a computer system, the processor configured to execute a method, the method comprising:

obtaining biometric data relating to the subject from a vibroacoustic sensor having a bandwidth ranging from 0.01 Hz to 160 kHz;

extracting an identification marker from the biometric data comprising at least one of cross-frequency coupling and cross-phase coupling from the biometric data; and

generating the unique algebraic lattice identifier from the extracted identification marker, wherein the generating the unique algebraic lattice identifier comprises identifying a given domain specific feature having a predetermined identity compared to other domain specific features.

18 . The system of claim 17 , further comprising the vibroacoustic sensor in a sensor array for obtaining the biometric data.