Biometric Authentication Using Multi-Source Metadata Matching
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Solution Overview
Problem
Current biometric authentication systems, particularly face recognition, face issues with recognition errors and security vulnerabilities, such as false accept/reject rates and susceptibility to attacks like presenting fake biometrics or modifying data, especially under poor lighting or aging conditions, which affect the accuracy and security of user authentication.
Innovation Solution
A method and system that captures a biometric sample, such as a facial image, and uses associated metadata like user contacts, employer, and location information to obtain and match multiple biometric samples from various sources, including social networks, to determine the authenticity of the user, employing techniques like deep learning image analysis and social graph matching to enhance authentication accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional biometric authentication systems are used, then the authentication process is simple and fast, but the system is vulnerable to attacks and has high false accept/reject rates
Solution Approach 1:
The patent combines multiple biometric samples from diverse sources (social media, public databases, personal devices) into a composite reference profile. This merging of multiple data sources creates a more robust authentication system that resists spoofing attacks while maintaining operational simplicity through automated data aggregation and comparison processes.
Solution Approach 2:
The system performs preliminary data gathering and profile creation by collecting biometric samples from various sources before authentication is needed. This advance preparation builds a comprehensive reference database that enables faster and more accurate real-time authentication decisions without increasing the complexity of the authentication moment itself.
2Ease of operation
If face recognition is used for convenience, then user interaction is simplified, but the system fails under poor lighting, low resolution, or aging conditions
Solution Approach 1:
The system transforms the authentication approach by changing from single-condition face recognition to multi-source biometric comparison. It aggregates biometric data across varying conditions (different lighting, resolutions, ages) from multiple sources, creating a reference profile that accommodates natural variations and maintains accuracy while preserving the convenience of automated recognition.
Solution Approach 2:
The patent creates a composite biometric profile by combining multiple biometric samples from different sources and conditions. This composite reference contains diverse representations of the user's appearance across various scenarios, enabling the system to maintain recognition accuracy under any single condition while preserving ease of operation through automated multi-source comparison.
3Reliability
If single-source biometric matching is used, then the process is fast, but the system is susceptible to attacks with fake biometrics
Solution Approach 1:
The system performs advance data collection and profile creation by gathering biometric samples from multiple sources before authentication is required. This preliminary action builds a comprehensive reference database that enables rapid real-time comparison without increasing authentication time, as the heavy lifting of data aggregation is completed in advance.
Solution Approach 2:
The patent creates multiple copies of biometric reference data from different sources (social media profiles, public databases, personal devices) to build a composite reference profile. This copying approach enables the system to compare against multiple representations simultaneously, detecting fake biometrics that would fail to match across diverse sources while maintaining fast authentication through efficient parallel comparison.
Data Source
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AI summary
There is presented a method, a computing device and a biometric matching service, for the biometric authentication of a user. The method comprises capturing a biometric sample from a user and obtaining information to identify data sources relevant to the user. The method further comprises using the data sources relevant to the user to obtain a plurality of biometric samples potentially captured from the user. The method further comprises matching the captured biometric sample against the plurality of potentially captured biometric samples to determine whether the captured biometric sample represents the user.