Multi-Modal Authentication With Dynamic Weighting and Incremental Learning
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Solution Overview
Problem
Interactive systems relying on single-modal biometric authentication and static learning models face limitations in security, adaptability, and user engagement due to their inability to provide a secure and personalized user experience.
Innovation Solution
Implementing a multi-modal biometric authentication system integrated with a Personalization and Learning Kernel (PLK) module that utilizes machine learning algorithms and data processing techniques, combining voice, facial, and textual biometric data with dynamic weighting based on environmental and contextual factors, and incorporating incremental learning, anomaly detection, and consensus algorithms to enhance security and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single-modal biometric authentication is used, then the system is simple to implement, but security and reliability are insufficient
Solution Approach 1:
The patent combines multiple biometric authentication modalities (facial recognition, voice recognition, fingerprint recognition) into a unified authentication system. The authentication module integrates these different modalities to perform multi-factor authentication, where each modality contributes to the overall authentication decision through a weighted scoring mechanism, thereby improving reliability while managing complexity through systematic integration
Solution Approach 2:
The patent employs a composite authentication approach by creating a weighted fusion of multiple biometric modalities. Each modality is treated as a component with its own reliability characteristics, and the system combines them using dynamically adjusted weights based on environmental factors, creating a composite authentication score that is more reliable than any single modality alone
2Adaptability or versatility
If static learning models are used, then the system is stable and predictable, but adaptability to varying conditions is poor
Solution Approach 1:
The patent implements dynamic weighting mechanisms where the contribution of each biometric modality is adjusted in real-time based on environmental conditions. The system continuously monitors factors such as lighting conditions, background noise, and sensor quality, and dynamically modifies the weights assigned to facial, voice, and fingerprint recognition accordingly, enabling the system to adapt to varying environmental conditions while maintaining stable authentication performance
Solution Approach 2:
The patent changes the parameters of the authentication system by introducing dynamically adjustable weights for each biometric modality. These weights are modified based on environmental parameters (lighting, noise levels, sensor quality) and contextual factors, allowing the system to optimize its performance across different operating conditions while maintaining a stable core authentication framework
3Reliability
If multi-modal biometric authentication with dynamic weighting is implemented, then security and reliability are improved, but device complexity increases
Solution Approach 1:
The patent segments the authentication system into distinct modular components: facial recognition module, voice recognition module, fingerprint recognition module, environmental sensing module, and authentication decision module. Each module operates independently with well-defined interfaces, allowing the complex multi-modal authentication system to be managed through systematic segmentation and modular integration
Solution Approach 2:
The authentication module is designed with universal functionality to handle multiple biometric modalities through a unified framework. The same authentication logic and weighted scoring mechanism apply regardless of which combination of biometric modalities is used, making the system multi-functional while avoiding the need for separate processing paths for each modality, thereby managing complexity
4Adaptability or versatility
If environmental and contextual factors are considered for dynamic weighting, then adaptability improves, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary assessment of environmental conditions and sensor capabilities before the actual authentication process. The system pre-evaluates lighting conditions, background noise levels, and sensor quality metrics, and uses these pre-assessed parameters to quickly determine the appropriate weights for each biometric modality, avoiding complex real-time calculations during the authentication moment and reducing processing time
Data Source
AI summary
Systems and methods and computer program products support authentication of a user for access to a computing resource. A method may include applying weights to preliminary results of machine learning (ML) models. The method may authenticate or not authenticate the user based upon whether the weighted sum of the preliminary results corresponds to an authentication condition.