Thermal Biometric Authentication Using Neural Network Liveliness Detection

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Solution Overview

Problem

Existing biometric authentication systems are susceptible to false authorization due to the inability to discern between authentic and imitated data samples, particularly with the advent of technologies like 3D printing that can create convincing replicas of user features.

Innovation Solution

A thermally enriched biometric authentication system that combines image data and thermal data from multiple channels, using a generative neural network to analyze isothermal curves and thermal gradient coloring, and performs liveliness detection to verify the authenticity of data samples by analyzing movement and thermal variations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional biometric authentication systems are used, then the authentication process is simple and fast, but the system is susceptible to false authorization from imitated data samples

Engineering Contradiction:

Improveauthentication accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent combines multiple biometric modalities (facial recognition, thermal imaging, gait analysis) into a unified authentication system. By merging these different sensing channels and processing them through a generative neural network, the system achieves higher reliability in distinguishing authentic from imitated samples, resolving the contradiction between accuracy and complexity through integrated multi-modal processing

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system transitions from traditional 2D image-based authentication to multi-dimensional authentication by incorporating thermal imaging data, temporal gait patterns, and 3D facial structures. This dimensional expansion allows the neural network to detect subtle characteristics that differentiate real from fake samples, improving reliability while the automated processing keeps complexity manageable

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If thermal imaging data is combined with image data, then the ability to detect authentic samples improves, but the processing time and computational requirements increase

Engineering Contradiction:

Improvedata authenticity detectionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing of thermal and image data streams separately before combining them in the neural network. Thermal data undergoes initial filtering and feature extraction, as does the visible image data. This preliminary action prepares the data for efficient integration and processing, reducing the time penalty that would otherwise result from handling multiple data types

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional sequential processing methods with a parallel generative neural network architecture that simultaneously processes multiple data modalities. This substitution of mechanical processing with intelligent automated processing reduces computational bottlenecks and minimizes processing time while maintaining high measurement precision for authenticity detection

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If multiple devices and channels are used to collect biometric data, then the verification confidence increases, but the system complexity and coordination requirements increase

Engineering Contradiction:

Improveidentity verification confidenceVSAvoidmulti-device coordination

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system employs a universal generative neural network architecture that can process data from multiple device types and sensing channels through a unified processing pipeline. This multi-functional approach allows the same core algorithm to handle facial data from cameras, thermal data from infrared sensors, and gait data from motion sensors, simplifying the coordination complexity while maintaining high verification confidence through multi-source data integration

Inventive Principle:

Principle #6Universality (Multi-functionality)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The system provides high-confidence identity verification by distinguishing between authentic and unauthorized data samples, reducing the likelihood of false authorizations and enhancing security through the integration of thermal imaging and machine learning.

Implementation Method 1

receiving image data and thermal data of an object from one or more devices and channels

Methodology Applied

Scientific EffectThermal radiation: Thermal Radiation

Data Source

PatentUS11269983B2Thermally enriched multi-modal and multi-channel biometric authentication

Publication Date: 2022.03.08 BANK OF AMERICA CORP

AI summary

Embodiments of the present invention provide an improvement to conventional biometric authentication systems and techniques by providing an innovative system, method and computer program product for thermal enrichment of biometric authentication data to generate a high-confidence verification of user identity. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. The invention provides for the enrichment of biometric authentication data with thermal imaging data in order to discern between authentic data samples in contrast to inanimate copies or models. Furthermore, the invention is designed to detect and analyze liveliness of data samples as a means of authentication.