Adaptive Reference Enrollment for AI Security Authentication
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Solution Overview
Problem
Traditional security systems face challenges in preventing false alarms and authenticating users effectively, particularly due to changes in user characteristics over time, and struggle with processing large volumes of event information.
Innovation Solution
A security system utilizing machine learning and deep learning algorithms to analyze image and audio information for authentication, including a two-stage approach with classical ML for initial processing and DL for enhanced accuracy, and integrating edge devices with remote servers for continuous user recognition and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion-based event generation is used to detect security events, then security monitoring capability is improved, but the volume of event information increases making it difficult for users to process
Solution Approach 1:
The patent extracts only the most relevant features from image and audio data using machine learning algorithms, rather than processing all raw event information. This selective extraction reduces the volume of information users need to process while maintaining security monitoring effectiveness.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the motion detection system and the user. These models process and filter the event information, translating raw data into meaningful security assessments that are easier for users to interpret and act upon.
2Measurement precision
If traditional authentication methods are used, then system simplicity is maintained, but authentication accuracy decreases due to user characteristic changes over time
Solution Approach 1:
The patent implements dynamic reference information that automatically updates to reflect changes in user characteristics over time. This dynamic adaptation allows the system to maintain high authentication accuracy without requiring manual reconfiguration, balancing precision with acceptable system complexity.
Solution Approach 2:
The machine learning system performs self-updating of reference information without requiring manual intervention. The system automatically adapts to user characteristic changes, reducing the operational complexity while maintaining high authentication accuracy.
3Measurement precision
If reference information is updated frequently to adapt to user changes, then authentication accuracy is improved, but system reliability may decrease due to potential false updates
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors the quality and validity of reference information updates. Machine learning algorithms analyze multiple data points before updating reference information, ensuring that updates are based on genuine user characteristic changes rather than anomalies, thus maintaining both accuracy and reliability.
Solution Approach 2:
The system performs preliminary validation and analysis before updating reference information. Machine learning models pre-process candidate reference data to assess its quality and relevance, preventing false or premature updates that could compromise system reliability while still adapting to legitimate user changes.
Data Source
AI summary
Person or object authentication can be performed using artificial intelligence-enabled systems. Reference information, such as for use in comparisons or assessments for authentication, can be updated over time to accommodate changes in an individual's appearance, voice, or behavior. In an example, reference information can be updated automatically with test data, or reference information can be updated conditionally, based on instructions from a system administrator. Various types of media can be used for authentication, including image information, audio information, or biometric information. In an example, authentication can be performed wholly or partially at an edge device such as a security panel in an installed security system.