AI Security System Multi-Factor Recognition
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Solution Overview
Problem
Traditional security systems struggle to effectively prevent security breaches by accurately identifying individuals with different access permissions using image or acoustic information, and they face challenges in processing the high volume of event information generated by motion-based events.
Innovation Solution
The system employs artificial intelligence-enabled analyzers to process candidate face information and gesture information from one individual, and other information from a second individual in proximity, to determine recognition results. These results are then used to conditionally grant or deny access to a protected area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If motion-based event generation is used to detect security events, then detection capability is improved, but the volume of event information increases making it difficult for users to process
Solution Approach 1:
The patent extracts and analyzes specific visual features (faces, gestures, objects) from the event information using image processing techniques. By extracting only the relevant visual elements and their characteristics, the system reduces the complexity of processing while maintaining detection capability. The system processes only the essential visual data rather than the entire event stream.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes image processing modules and machine learning classifiers. These intermediaries automatically analyze and filter event information, translating raw sensor data into meaningful security assessments. The intermediary system handles the complexity of processing large volumes of event information automatically.
2Reliability
If multiple factors are used for recognition and validation, then security accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the recognition system into distinct modules: face detection module, gesture recognition module, object detection module, and machine learning classification module. Each module handles a specific aspect of validation independently. This segmentation allows the system to achieve multiple-factor recognition while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent replaces complex manual validation processes with automated machine learning-based recognition systems. Instead of requiring complex mechanical or manual verification procedures, the system uses AI algorithms to automatically perform multi-factor validation, simplifying the overall system implementation while improving accuracy.
Data Source
AI summary
A security system can conditionally grant or deny access to a protected area using an artificial intelligence system to analyze images. In an example, an access control method can include receiving candidate information about a face and gesture from a first individual and receiving other image information from or about a second individual. The candidate information can be analyzed using a neural network-based recognition processor that can provide a first recognition result indicating whether the first individual corresponds to a first enrollee of the security system, and can provide a second recognition result indicating whether the second individual corresponds to a second enrollee of the security system. The example method can include receiving a passcode, such as from the first individual. Access can be conditionally granted or denied based on the passcode and the recognition results.