Dynamic Pressure Biometric Identification via Temporal Sensing
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Solution Overview
Problem
Current biometric identification methods rely on static data, making them vulnerable to duplication and reproduction, which can be exploited by attackers to gain unauthorized access.
Innovation Solution
A secure biometric identification system using dynamic pressure sensing that captures and analyzes temporal and spatial pressure data from users, employing machine learning to create unique user profiles and a hybrid database structure that is difficult to replicate, ensuring high security through volatility index calculations and adaptive confidence intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static biometric data is used for identification, then the system is simple to implement, but the security is vulnerable to duplication and reproduction by attackers
Solution Approach 1:
The patent transitions from static biometric data to dynamic pressure patterns that change over time. The system captures pressure data at multiple time points during user interaction, creating a dynamic profile that is difficult to replicate. This temporal dimension adds complexity to the biometric data structure, making it resistant to copying while maintaining implementability through standard pressure sensing hardware.
Solution Approach 2:
The patent introduces a temporal dimension to pressure data collection, moving from single-point static measurements to multi-point dynamic sequences. By capturing pressure patterns across time (t1, t2, t3, etc.), the system creates a multi-dimensional data structure that significantly increases security difficulty for attackers while remaining grounded in physical pressure sensing technology.
2Reliability
If dynamic pressure sensing is used for identification, then the security becomes virtually impossible to copy, but the device complexity increases
Solution Approach 1:
The patent segments the pressure data collection process into multiple discrete time points (t1, t2, t3, etc.) during user interaction. Each time point captures a snapshot of pressure distribution, and these segmented measurements are combined to create the final dynamic profile. This segmentation approach manages complexity by breaking down the data collection into manageable temporal steps while maintaining high security through the cumulative complexity of the full pattern.
Solution Approach 2:
The system incorporates feedback mechanisms where the processed pressure patterns are used to generate user profiles that are stored and compared against future interactions. The feedback loop continuously refines the identification process by comparing new pressure patterns against established profiles, adapting to variations in user behavior while maintaining consistent identification. This feedback mechanism manages system complexity through iterative processing rather than requiring overly complex one-time calculations.
Data Source
AI summary
Described herein are systems and methods for secure biometric identification using dynamic pressure sensing that are convenient and intuitive to use. Accurate identification is accomplished by using a set of finely spaced analog sensors that measure and output a dynamic pressure profile that is then evaluated based on data from a trained model. The model comprises a number of personal biometric characteristics that may be used to uniquely identify a person, e.g., for authentication purposes, such as granting access to sensitive, confidential information in connection with an electronic commercial transaction, an Internet of Things (IoT) device, an automotive device, an identity and access management (IAM), or a robotic or high functioning touch sensing device.