Dynamic Face Verification Threshold Adjustment
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Solution Overview
Problem
Face verification technologies face challenges in dynamically adjusting verification thresholds based on varying image conditions, such as face region size, eyelid closure, liveness, and image brightness, which affects the accuracy and reliability of authentication processes.
Innovation Solution
A processor-implemented verification method that analyzes current frames of verification images, determines their state scores based on factors like face region size, eyelid closure, liveness, and image brightness, and dynamically sets verification thresholds to ensure accurate comparison with registered features, discarding inappropriate frames and adjusting thresholds to maintain verification difficulty and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed verification threshold is used for face verification, then the verification process is simple and fast, but the accuracy and reliability of authentication deteriorates under varying image conditions
Solution Approach 1:
The verification threshold is dynamically adjusted based on the current frame state score, which reflects image quality conditions such as face region size, eyelid closure, liveness, and brightness. This dynamic adjustment mechanism allows the system to maintain high authentication reliability under varying conditions without requiring manual intervention or complex fixed threshold configurations.
Solution Approach 2:
The system changes the verification threshold parameter based on analyzed image conditions. By computing a state score from multiple image quality parameters and using this score to adjust the threshold, the system adapts to different verification scenarios, improving reliability while keeping the adjustment process automated and relatively simple.
2Reliability
If the verification threshold is strictly increased to reduce false acceptance, then authentication security is improved, but false rejection rate increases
Solution Approach 1:
The verification threshold dynamically adapts to image quality conditions through the state score mechanism. When image conditions are poor (e.g., small face region, high eyelid closure), the threshold is adjusted to be more lenient, reducing false rejections. When conditions are good, the threshold can be stricter, maintaining security. This dynamic behavior resolves the contradiction between security and accuracy.
Solution Approach 2:
The system changes the verification threshold parameter based on the computed state score from image analysis. This parameter adaptation allows the system to optimize the balance between false acceptance and false rejection rates according to actual image conditions, improving overall verification accuracy while maintaining security.
3Reliability
If all frames are processed for verification, then verification completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of each frame to compute a state score before actual verification processing. Frames with state scores below a certain threshold are discarded early, avoiding unnecessary computational resources. This preliminary filtering action maintains verification completeness for suitable frames while significantly reducing processing time by eliminating obviously unsuitable frames.
Solution Approach 2:
The system extracts and processes only the relevant frames that meet the state score criteria, separating them from unsuitable frames. By taking out only the appropriate frames for verification and discarding the rest, the system maintains completeness for valid cases while reducing overall processing time and resource consumption.
Data Source
AI summary
Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.