Dynamic Threshold Facial Verification for Security Inspection
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Solution Overview
Problem
Current security inspection methods, relying on human-certificate verification machines, face inefficiencies and misrecognition issues, leading to prolonged verification times and increased security risks due to fixed similarity thresholds that fail to account for varying environmental and personal factors.
Innovation Solution
A method and apparatus for security inspection that dynamically determines the facial comparison similarity threshold using a dynamic threshold model generated from historical verification data, incorporating identity-related information such as nationality, age, and gender, through large data visualization and machine learning algorithms, enabling accurate and fast verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed similarity threshold is used for facial comparison, then the verification process is simple and fast, but the accuracy decreases leading to misrecognition phenomena
Solution Approach 1:
The patent applies dynamics by transforming the fixed similarity threshold into a dynamic threshold that adapts to different verification scenarios. The system determines thresholds based on multiple factors including facial image quality, environmental conditions, and historical verification data, allowing the threshold to vary rather than remain static. This resolves the contradiction by enabling accurate comparisons without requiring overly complex manual intervention.
Solution Approach 2:
The patent changes the parameter of the similarity threshold from a fixed value to a variable determined by multiple parameters including image quality metrics, environmental factors, and statistical analysis of historical data. By changing how the threshold parameter is determined rather than changing the threshold value itself, the system improves accuracy while controlling complexity through automated parameter adjustment.
2Reliability
If multiple comparisons are performed to ensure accuracy, then the reliability improves, but the verification time increases significantly
Solution Approach 1:
The patent applies preliminary action by performing quality assessment and threshold determination before the actual facial comparison. The system pre-processes images to assess quality, pre-determines appropriate thresholds based on historical data, and pre-identifies potential issues. This allows the actual comparison to be performed quickly with high confidence, reducing the need for multiple re-comparisons and thus reducing verification time while maintaining reliability.
Solution Approach 2:
The patent implements feedback mechanisms where historical verification results are continuously analyzed to refine threshold determination. The system learns from past comparisons and adjusts thresholds dynamically, improving reliability over time without requiring increasingly multiple comparisons. This feedback loop enables the system to achieve high reliability with fewer comparisons by continuously optimizing its decision criteria.
3Measurement precision
If human verification is used to resolve misrecognition, then the accuracy improves, but the productivity decreases due to manual intervention requirements
Solution Approach 1:
The patent applies self-service by enabling the automated verification system to resolve most cases independently through dynamic threshold adjustment and quality-based comparison. The system autonomously assesses image quality, determines appropriate thresholds, and makes verification decisions without requiring human intervention for routine cases. This maintains high accuracy while preserving productivity by minimizing manual involvement to only truly ambiguous cases.
Solution Approach 2:
The patent changes the operational parameters of the verification system to reduce reliance on human intervention. By adjusting thresholds dynamically based on image quality and historical data, the system achieves accuracy levels that reduce the need for manual review. This parameter optimization allows automated processing to handle more cases correctly, thereby maintaining both accuracy and productivity.
4Reliability
If the verification system is made more stringent to reduce misrecognition, then the security improves, but the verification speed decreases
Solution Approach 1:
The patent applies dynamics by making the verification stringency adaptive rather than fixed. The system dynamically adjusts the effective threshold based on image quality assessment and contextual factors. When images are high quality, the system can be more stringent without significantly impacting speed. When images are lower quality, the system automatically adjusts to maintain appropriate verification standards while preserving speed. This dynamic adaptation resolves the contradiction between security and speed.
Data Source
AI summary
Disclosed in the present disclosure are a method and an apparatus for security inspection. The method comprises: acquiring identity related information of a person being inspected, the identity related information comprising a certificate photo; acquiring a real-time facial image of the person being inspected; by comparing the real-time facial image and the certificate photo, acquiring a facial comparison similarity; dynamically determining the threshold of the facial comparison similarity; and performing a human-certificate verification on the person being inspected according to the facial comparison similarity and the threshold. The method for security inspection of the present disclosure enables the fast and accurate facial comparison of the person being inspected during security inspection.