Biometric Authentication System Using Dynamic Neural Profiles
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Solution Overview
Problem
Current identification systems rely on static credentials that can be hijacked or forged, and they authenticate credentials rather than the person presenting them, leading to vulnerabilities in personal and physical security, especially in the face of increasing impersonation attacks.
Innovation Solution
A device configured to capture and monitor human traits through sensors, using machine learning to create a unique profile of the user, allowing real-time authentication without the need for traditional credentials, by leveraging a combination of physiological, environmental, and human activity traits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static credentials (passwords, keys) are used for authentication, then ease of operation is improved, but reliability deteriorates due to hijacking and forging vulnerabilities
Solution Approach 1:
The patent transitions from static credentials to dynamic biometric authentication. The system captures real-time biometric data (facial features, voice patterns, gait analysis) and compares it against stored profiles, ensuring authentication adapts to current physiological states rather than relying on fixed passwords that can be compromised.
Solution Approach 2:
The patent replaces mechanical credential verification (checking passwords, tokens) with biological trait analysis. Sensors capture physiological signals (facial recognition cameras, voice microphones, motion detectors) and use machine learning algorithms to authenticate users based on inherent biological characteristics that are difficult to replicate.
2Reliability
If multiple authentication factors are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple authentication factors into a unified biometric system. Instead of requiring separate devices for passwords, tokens, and biometrics, the system integrates facial recognition, voice analysis, and gait detection into a single authentication flow, reducing the perceived complexity for users while maintaining multi-factor security.
Solution Approach 2:
The patent creates a multi-functional authentication system where a single device performs multiple authentication tasks. The same sensor array captures facial features, voice patterns, and motion data, allowing one device to replace multiple specialized authentication tools while maintaining comprehensive security.
3Measurement precision
If continuous monitoring of human traits is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic biometric monitoring instead of continuous tracking. The system captures biometric data at intervals (e.g., during device unlock, app access, or scheduled checks) rather than continuously recording all physiological signals, significantly reducing power consumption while maintaining authentication accuracy when needed.
Solution Approach 2:
The patent selectively processes biometric data based on contextual needs. Not all sensors operate at full capacity simultaneously; the system activates specific sensors based on the authentication context (e.g., facial recognition for device unlock, voice analysis for sensitive operations) and discards unnecessary data collection to conserve energy.
Data Source
AI summary
Within a mobile device, a method and system to produce a probability the mobile device is in possession of a known person, the first user. Sensors are used to detect and quantify the behavioral biometrics of the human traits of the person in possession of the device. On a continuous basis, a machine learning process collects the biometrics of several traits of the first user memorizing the artifacts of neural networks used for learning. Subsequently, a prediction neural network provisioned with these artifacts and processing new biometric inputs of the present user of the device produces a probability the present user of the device is the first user. Affirmation of identity can then be made based on that probability.