Decision-Level Multi-Biometric Authentication For Battery-Powered IoT
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Solution Overview
Problem
Conventional biometric systems face challenges such as increased energy consumption, complexity, and latency due to the use of multiple biometric traits, and reliance on cloud servers for processing, which is not suitable for battery-powered IoT devices.
Innovation Solution
An energy-efficient multi-biometric authentication system incorporating a trust management system and decision-level multi-biometric approach, using a fingerprint scanner and camera, with a trust value-based decision mechanism to reduce energy consumption and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple biometric traits are used to improve authentication accuracy, then authentication reliability is improved, but energy consumption and system complexity increase
Solution Approach 1:
The system dynamically adjusts the number and type of biometric traits used for authentication based on the trust value of the user. Trusted users are authenticated using fewer biometric traits (e.g., fingerprint only), while less trusted users require multiple biometric traits (e.g., fingerprint + face recognition). This dynamic adaptation resolves the contradiction by making the system flexible rather than static, allowing it to optimize between security and energy consumption for each user context.
Solution Approach 2:
The patent applies different authentication strategies to different users based on their individual trust values. Each user has a personalized trust value that determines the authentication requirements. This local differentiation allows the system to provide high security to low-trust users while offering energy-efficient authentication to high-trust users, resolving the contradiction at the individual user level rather than applying a uniform approach to all users.
2Reliability
If multiple biometric traits are used to improve authentication accuracy, then authentication reliability is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects which biometric modalities to activate based on trust values, making the complexity manageable. Instead of always running complex multi-biometric authentication, the system adapts its complexity level - using simple fingerprint authentication for trusted users and complex multi-biometric authentication only when necessary for less trusted users.
Solution Approach 2:
The authentication process is segmented into different levels or tiers based on user trust. The system divides users into categories (trusted vs. less trusted) and applies different authentication procedures to each segment. This segmentation allows the complex multi-biometric system to be broken down into manageable components that are only activated when needed.
3Device complexity
If cloud servers are used for processing to reduce local computational requirements, then device complexity is reduced, but communication costs and data transmission requirements increase
Solution Approach 1:
The system performs preliminary biometric extraction and feature generation locally on the device before any cloud communication occurs. By preparing and processing biometric data locally in advance, the system minimizes the amount of data that needs to be transmitted to the cloud, reducing communication overhead while still benefiting from cloud-based authentication services.
Solution Approach 2:
The patent extracts and processes only the essential biometric features and trust evaluation logic locally on the device, separating these functions from the cloud server. This extraction allows the device to operate with reduced complexity by handling critical authentication decisions locally rather than relying entirely on cloud processing.
4Use of energy by moving object
If conventional single-biometric systems are used to reduce energy consumption, then energy efficiency is improved, but authentication reliability decreases due to false positives and false negatives
Solution Approach 1:
The system dynamically adjusts the authentication strategy based on real-time trust value assessments. For users with high trust values, the system uses single-biometric authentication to minimize energy consumption. For users with low trust values, the system activates multi-biometric authentication to improve reliability. This dynamic switching allows the system to optimize the trade-off between energy efficiency and authentication reliability for each user context.
Solution Approach 2:
The system incorporates feedback mechanisms where authentication results and user behavior patterns are used to continuously update and refine trust values. This feedback loop allows the system to learn from past authentication performance and adjust future authentication strategies accordingly, improving reliability over time while maintaining energy efficiency for trusted users.
Data Source
AI summary
A computer system and method having an energy-efficient multi-biometric authentication that includes a fingerprint scanner, a camera, a memory, and a processing circuitry. The processing circuitry reads a predefined probability from the memory and generates a random number. The processing circuitry inputs a user ID and retrieves a trust value for the user from a trust database. The processing circuitry obtains a scanned user fingerprint using a fingerprint scanner and obtains a captured user face using the camera. The processing circuitry applies a reward to the trust value to increase the trust value when the scanned user fingerprint is substantially the same as the stored user fingerprint and the captured user face is substantially the same as a stored user face in a face database, store the trust value as the cumulative trust value for the user, and authenticate the user to allow access to the computer system.