AI Biometric Authentication Using Context-Aware Verification
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Solution Overview
Problem
Biometric authentication systems often fail due to changes in human biometrics caused by contextual factors such as weather, time of day, and user behavior, leading to incorrect authentication or denial of access.
Innovation Solution
A machine-learning model is trained to consider contextual parameters like geolocation, weather conditions, and user behavior to improve biometric authentication success by adjusting to variations in user biometrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional biometric authentication methods are used, then the authentication process is simple and fast, but the reliability decreases when contextual parameters change
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the biometric sensor and the authentication decision system. This model processes contextual parameters (weather, time, location) and biometric data together to make more reliable authentication decisions, especially when conventional methods fail due to environmental factors.
Solution Approach 2:
The system changes the parameters considered during authentication by incorporating contextual parameters (weather conditions, time of day, geolocation) alongside traditional biometric data. This multi-parameter approach allows the system to adapt to varying conditions and maintain reliability when environmental factors affect biometric characteristics.
2Measurement precision
If biometric authentication is performed without considering contextual parameters, then the system is easy to operate, but measurement precision decreases
Solution Approach 1:
The machine learning model automatically processes and weighs multiple parameters (biometric data, weather, time, location) without requiring user intervention. The system self-adjusts its authentication criteria based on contextual factors, improving measurement precision while maintaining ease of operation as users simply provide their biometric data without needing to understand or input contextual information.
3Reliability
If a machine-learning model considering contextual parameters is implemented, then authentication reliability improves, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing contextual parameters (weather data, time information, geolocation) before the authentication decision is needed. The machine learning model is also pre-trained with extensive data to recognize patterns and make accurate predictions, reducing the computational complexity during actual authentication operations.
Solution Approach 2:
The machine learning model serves multiple functions: it processes biometric data, evaluates contextual parameters, identifies authentication failures due to environmental factors, and makes final authentication decisions. This multi-functionality consolidates what could be separate complex systems into a single versatile component.
Data Source
AI summary
In one embodiment, a method includes receiving biometrics of a user to access the electronic device. The method may access one or more contextual parameters affecting a state of the user. The method may verify, using a trained machine-learning model, the biometrics of the user based on the one or more contextual parameters affecting the state of the user. The method may provide access to the electronic device in response to successful verification.