Enabling continuous or instantaneous identity recognition of a large group of people based on physiological biometric signals obtained from members of a small group of people

• US 9,646,261 B2
• Filed: 05/10/2012
• Issued: 05/09/2017
• Est. Priority Date: 05/10/2011
• Status: Active Grant

• Current Assignee: Nymi Inc.
• Original Assignee: Nymi Inc.
• Inventors: Agrafioti, Foteini, Bui, Francis Minhthang, Hatzinakos, Dimitrios
• Primary Examiner(s): VINCENT, DAVID ROBERT
• Application Number:
  US14/116,058
• Publication Number:
  US 20140188770A1
• Time in Patent Office: 1,825 Days
• Field of Search: 706 12, 706 45
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 21/316 by observing the pattern of computer usage, e.g. typical user behaviour
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/12 Fingerprints or palmprints
• CPC Class Codes: G06V 40/15 Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: H04L 63/0861 using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231

Abstract

The present invention is a biometric security system and method operable to authenticate one or more individuals using physiological signals. The method and system may comprise one of the following modes: instantaneous identity recognition (MR); or continuous identity recognition (CIR). The present invention may include a methodology and framework for biometric recognition using physiological signals and may utilize a machine learning utility. The machine learning utility may be presented and adapted to the needs of different application environments which constitute different application frameworks. The present invention may further incorporate a method and system for continuous authentication using physiological signals and a means of estimating relevant parameters.

Claims

1. A biometric security system operable to authenticate one or more individuals, said system characterized in that it comprises:

a) a device operable to obtain one or more physiological signals of each of the one or more individuals, wherein the one or more individuals are initially enrolled in the biometric security system and the one or more individuals are members of a small scale frame work of individuals that were previously identified before the device is employed to currently obtain the one or more physiological signals;

b) a machine learning utility connected to the device, said machine learning utility being operable to biometrically process the one or more physiological signals to determine a variability of physiological signals for a larger population of the one or more individuals based on the one or more physiological signals obtained from the individual members of the small scale frame work, and to identify or verify the identity of each of the one or more individuals, wherein the machine learning utility is trained with recordings of one or more older physiological signals that were previously obtained from the previously identified individuals that are members of the small scale frame work and the larger population of the one or more individuals; and

c) one or more databases connected to the machine learning utility operable to store one or more biometrically processed physiological signals.

2. The biometric security system of claim 1, characterized in that the machine learning utility is operable in any of the following modes:

continuous identity recognition mode; and

instantaneous recognition mode.

3. The biometric security system of claim 2, characterized in that the device is operable to obtain the one or more physiological signals of the one or more individuals on a continuous basis during a period of time when operating in the continuous identity recognition mode.

4. The biometric security system of claim 2, characterized in that the device when operating in the continuous identify recognition mode is operable to estimate cumulative biometric confidence by assessing the progression of one or more biometric decisions in reference to time.

5. The biometric security system of claim 4, characterized in that the cumulative biometric confidence estimate is operable in relation to one or more alarm outputs, whereby when an alarm occurs the cumulative biometric confidence increases, and this initializes a period of suspected intrusion monitoring, during which the cumulative confidence will either increase or remain at a previous level.

6. The biometric security system of claim 1, characterized in that the device incorporates a display means operable to display the authentication results of the system to each of the one or more individuals.

7. The biometric security system of claim 1, characterized in that the device incorporates an input means operable for any of the one or more individuals to input an identity claim to provide identification data that is any of the following:

a name;

a password;

a device ID number; and

other non-biometric data identifying the one of the one or more individuals inputting the identity claim.

8. The biometric security system of claim 1, characterized in that the one or more databases include any of the following:

a generic database, an enrollee database, or an enrollee and generic database.

9. The biometric security system of claim 8, characterized in that the generic database is operable to store one or more template biometric signals generated by a biometric template design module so that the one or more template biometric signals are accessible by the machine learning utility to determine the variability of each of the one or more physiological signals in a population.

10. The biometric security system of claim 1, characterized in that it is a distributed system or a centralized system.

11. The biometric security system of claim 1, characterized in that a variability estimation means operable to generate a threshold output is connected to the machine learning utility.

12. The biometric security system of claim 1, characterized in that any of the following are connected to the machine learning module and are operable to biometrically process the one or more physiological signals:

a filter module;

an autocorrelation module; and

an outlier removal module.

13. The biometric security system of claim 1, characterized in that a feature projection module is incorporated in an identification or verification mode of the system to apply a transformation rule to biometrically process the one or more physiological signals, said feature projection module being connected to a matching module.

14. The biometric security system of claim 13, characterized in that the feature projection module is connected to any of the following:

a) a one-to-many matching module operable to determine the identity of the one of the one or more individuals relating to the one or more physiological signals in the identification mode of the system; and

b) a one-to-one matching module operable to receive an identity claim and to utilize the identity claim to determine whether one of the one or more physiological signals is that of one of the one or more individuals.

15. The biometric security system of claim 1, characterized in that it is operable in a large-scale framework or a small-scale framework.

16. A biometric security method to authenticate one or more individuals, said method characterized in that it comprises the steps of:

a) employing a device to receive one or more physiological signals from one or more individuals that are initially enrolled in the biometric security system, wherein the one or more individuals are members of a small scale frame work of individuals that were previously identified before the device is employed to currently receive the one or more physiological signals;

b) employing a machine learning utility to pre-process each of the one or more physiological signals to generate a biometrically processed signal and determine a variability of physiological signals for a larger population of the one or more individuals based on the one or more physiological signals obtained from the individual members of the small scale frame work, wherein the machine learning utility is trained with recordings of older one or more physiological signals that were previously received from the previously identified individuals that are members of the small scale frame work and the larger population of the one or more individuals;

c) employing the device to extract features from the biometrically processed signal to generate a set of signal features; and

d) employing the machine learning utility to classify the set of signal features to identify or verify the identity of each of the one or more individuals.

17. The biometric security method of claim 16, characterized in that it comprises one or more of the further steps of:

a) wherein the pre-processing of each of the one or more physiological signals includes generating the biometrically processed signal by any of the steps of;

filtering and windowing;

b) wherein the extracting of features of the biometrically processed signal includes generating the set of signal features by any of the steps of;

autcorrelation estimation;

outlier removal; and

machine learning incorporating linear discriminant analysis;

or

c) employing the machine learning utility to classify the set of signal features by any of the steps of;

matching;

individual confidence estimation; and

cumulative confidence estimation.

18. The biometric security method of claim 16, characterized in that it comprises the further step of employing the device to continuously receive the one or more physiological signals relating to at least one of the one or more individuals during a time period and employing the machine learning utility for repeating the following steps for each of the one or more physiological signals to perform continuous recognition identification of at least one of the one or more individuals:

a) pre-processing each of the one or more physiological signals to generate a biometrically processed signal;

b) extracting features from the biometrically processed signal to generate a set of signal features; and

c) classifying the set of signal features to identify or verify the identity of each of the one or more individuals.

19. The biometric security method of claim 16, characterized in that it comprises employing the device to perform the further step of receiving an identity claim from at least one of the one or more individuals and utilizing the identity claim in the verification of the identity of the one of the one or more individuals who provided the identity claim.

20. The biometric security method of claim 16, characterized in that it comprises employing the device to perform the further step of generating any of:

a transformation rule; and

a threshold.

21. The biometric security method of claim 16, characterized in that it comprises employing the device to perform the further step of applying a biometric algorithm or calculation to identify or verify the identity each of the one or more individuals.

22. The biometric security method of claim 16, characterized in that it comprises the further step of employing the device to perform biometric confidence analysis in any of the following manners:

continuously; and

cumulatively.