System for biometric identity confirmation

• US 9,274,509 B2
• Filed: 01/11/2013
• Issued: 03/01/2016
• Est. Priority Date: 01/20/2012
• Status: Active Grant

• Current Assignee: Lifeloc Technologies Inc. (Edco Partners LLLP)
• Original Assignee: Integrated Monitoring Systems, LLC (Track Group, Inc.)
• Inventors: Phillips, Brian K., Wilson, Geoffrey A.
• Primary Examiner(s): Zimmerman, Brian
• Assistant Examiner(s): Patel, Bhavin M
• Application Number:
  US13/739,224
• Publication Number:
  US 20130187757A1
• Time in Patent Office: 1,145 Days
• Field of Search: 340/5.82
• US Class Current: 1/1
• CPC Class Codes: G05B 1/01 electric
• CPC Class Codes: G06F 21/32 using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06F 2221/2117 User registration
• CPC Class Codes: G06V 40/45 Detection of the body part being alive

Abstract

A biometric identity confirmation system is based on both pulse wave data and spirometric data for the subject. During an initial enrollment mode, pulse wave and spirometric data for a known subject are used to generate subject characterization data for the known subject. During a subsequent identity authentication mode, pulse wave and spirometric data for a test subject are analyzed using the subject characterization data to confirm whether the identity of the test subject matches the known subject.

Claims

1. A method for biometric identity confirmation of a subject having a pulse and a respiratory cycle, said method comprising:

during an initial training mode, simultaneously acquiring pulse wave shape data and spirometric data from a known subject;

generating and storing subject characterization data for the known subject derived at least in part from both the pulse wave shape data and spirometric data for the known subject; and

during a subsequent identity authentication mode, simultaneously acquiring pulse wave shape data and spirometric data from a test subject, and analyzing the pulse wave shape data and spirometric data with the subject characterization data for the known subject to confirm whether the identity of the test subject matches the known subject;

wherein the step of generating subject characterization data further comprises use of a second derivative of the pulse wave shape data with respect to time to identify pulse cycle start points for synchronous averaging of multiple pulse cycles to create an exemplar for the known subject.

2. The method of claim 1 wherein the subject characterization data is derived at least in part from first derivative of the pulse wave shape data with respect to time.

3. The method of claim 1 wherein the second derivative of the pulse wave shape data is used to identity trigger candidates for the pulse cycle start point;

and further comprising an analysis of a time delay between trigger candidates to thereby discard false trigger candidates and identify true trigger candidates for synchronous averaging of multiple pulse cycles.

4. The method of claim 1 wherein the step of generating subject characterization data further comprises:

computing an exemplar in the form of a parameter vector from the pulse wave shape data and spirometric data for the known subject;

computing a covariance matrix from the pulse wave shape data and spirometric data for the known subject;

computing an optimal composite parameter from the covariance matrix and parameter vector that is characteristic of the known subject; and

computing an authentication threshold corresponding to a desired true authentication probability for the known subject.

5. The method of claim 4 wherein the step of analyzing the pulse wave shape data and spirometric data with the subject characterization data for the known subject to confirm whether the identity of the test subject matches the known subject further comprises:

computing a deviation of the pulse wave shape data and spirometric data for the test subject from the exemplar for the known subject;

computing an optimal composite parameter from the deviation; and

confirming the identity of the test subject matches the known subject if optimal composite parameter is greater than the authentication threshold for the known subject.

6. The method of claim 1 wherein the step of generating subject characterization data further comprises:

computing an exemplar in the form of a parameter vector from the pulse wave shape data and spirometric data for the known subject;

computing a covariance matrix from the pulse wave shape data and spirometric data for the known subject; and

computing a probability distribution ratio of a weighted subject/impostor probability density by a Bayesian optimal decision analysis of the parameter vector, covariance matrix, and data from other subjects as potential impostors for the known subject.

7. The method of claim 6 wherein the step of analyzing the pulse wave shape data and spirometric data with the subject characterization data for the known subject to confirm whether the identity of the test subject matches the known subject further comprises:

computing a deviation of the pulse wave shape data and spirometric data for the test subject from the exemplar for the known subject;

computing a weighted subject/impostor probability density ratio for the deviation; and

confirming the identity of the test subject matches the known subject if the weighted subject/impostor probability density ratio is greater than zero.

8. The method of claim 1 wherein the step of generating subject characterization data further comprises:

computing an exemplar from the pulse wave shape data by synchronous averaging of multiple pulse cycles; and

weighting portions of the exemplar selected based on their repeatability observed during the initial training mode.

9. The method of claim 1 wherein the step of generating subject characterization data further comprises:

computing an exemplar from the pulse wave shape data by synchronous averaging of multiple pulse cycles; and

weighting portions of the exemplar selected to distinguish characteristic features of the known subject observed during the initial training mode.

10. A method of claim 1 wherein the step of generating subject characterization data for the known subject further comprises measurement of characteristic changes in the pulse wave shape data as a function of the phase of the respiratory cycle of the known subject.