Biometric Security Using Physiological Signals for Continuous Authentication

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Solution Overview

Problem

Traditional biometric security technologies face challenges in robustness against circumvention, replay attacks, and obfuscation, particularly when using external physiological features like fingerprints or iris scans, which can be forged or altered, and lack continuous authentication capabilities.

Innovation Solution

A biometric security system and method utilizing physiological signals, such as electrocardiogram (ECG) and photoplethysmographic (PPG) signals, that employs machine learning for continuous and instantaneous identity recognition, incorporating features like autocorrelation, Linear Discriminant Analysis (LDA), and outlier removal to provide secure and continuous authentication.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional biometric modalities (fingerprints, iris scans) are used for identity recognition, then the system is easy to operate and implement, but the security robustness against circumvention, replay attacks, and obfuscation is insufficient

Engineering Contradiction:

Improvesecurity robustnessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent transitions from using traditional external biometric features (fingerprints, iris) to internal physiological signals (ECG, PPG, respiration) that are harder to forge. This parameter change in the type of biometric data fundamentally improves security robustness while maintaining operational simplicity through automated signal acquisition and machine learning-based recognition.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual biometric capture methods with automated physiological signal acquisition using sensors that continuously monitor internal body signals. Machine learning algorithms substitute for traditional matching mechanisms, enabling robust security verification through patterns in ECG, PPG, and respiration signals that are difficult to replicate or spoof.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional biometric systems are used, then the implementation is straightforward, but continuous authentication capability is lacking

Engineering Contradiction:

Improvecontinuous authentication capabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements continuous authentication by continuously monitoring physiological signals (ECG, PPG, respiration) rather than performing discrete biometric checks. The system maintains an ongoing assessment of user identity through continuous signal acquisition and analysis, enabling real-time verification throughout the session rather than single-point authentication.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system performs self-service continuous authentication by automatically acquiring and analyzing physiological signals without requiring active user participation. The machine learning model continuously processes the signals and maintains authentication status autonomously, freeing the user from repeated verification actions while providing persistent security.

Inventive Principle:

Principle #25Self-service

3Reliability

If physiological signals are used for biometric recognition, then security robustness is improved, but the difficulty of detecting and measuring the signals increases

Engineering Contradiction:

Improvesecurity robustnessVSAvoidsignal detection difficulty

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent uses machine learning algorithms as intermediaries to bridge the gap between raw physiological signals and reliable identity recognition. The ML models process and interpret the complex ECG, PPG, and respiration signals, extracting discriminative features that enable accurate authentication while handling the inherent noise and variability in these biological signals.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system employs multi-functional physiological signal acquisition that serves both health monitoring and biometric authentication purposes. The same sensors that monitor cardiac and respiratory health also capture unique physiological patterns for identification, eliminating the need for separate dedicated biometric hardware and reducing overall system complexity despite the advanced signal processing requirements.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS9646261B2Enabling 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

Publication Date: 2017.05.09 NYMI

AI summary

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.