Face Recognition Threshold Adjustment for Stable Match Rates
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Solution Overview
Problem
Face recognition systems struggle with maintaining accurate positive determination rates under varying environmental conditions, leading to either false negatives or false positives due to static decision thresholds.
Innovation Solution
A system that automatically adjusts the decision threshold based on the current positive determination rate, increasing or decreasing it to maintain a target positive determination rate by dynamically adapting to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a static decision threshold is used for face recognition, then the system is simple to operate, but the positive determination rate becomes inaccurate under varying environmental conditions
Solution Approach 1:
The patent applies dynamics by transitioning from a static decision threshold to a dynamic one that automatically adjusts based on environmental conditions. The system monitors performance metrics and adapts the threshold in real-time, allowing the face recognition system to maintain accurate positive determination rates while remaining easy to operate through automated adjustment.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors face recognition performance and uses this information to adjust the decision threshold. This closed-loop approach ensures the threshold remains optimized for current environmental conditions, resolving the contradiction between operational simplicity and determination accuracy.
2Measurement precision
If the decision threshold is increased to reduce false positives, then the precision of positive determinations improves, but the number of false negatives increases
Solution Approach 1:
The system dynamically adjusts the decision threshold based on real-time performance monitoring. When false positives are detected, the threshold is increased to improve precision; when false negatives increase, the threshold is decreased to reduce missed detections. This dynamic adaptation resolves the trade-off between precision and false negative rate.
Solution Approach 2:
The patent changes the decision threshold parameter automatically based on system performance and environmental conditions. By adjusting this critical parameter in response to monitored metrics, the system optimizes the balance between precision of positive determinations and the rate of false negatives, resolving the technical contradiction.
3Reliability
If the decision threshold is decreased to reduce false negatives, then the sensitivity of face recognition improves, but the number of false positives increases
Solution Approach 1:
The system uses feedback from performance monitoring to automatically adjust the decision threshold. When false negatives are detected, the threshold is decreased to improve sensitivity; when false positives increase, the threshold is increased to improve precision. This feedback-driven adjustment resolves the contradiction between false negative rate and precision.
4Device complexity
If a fixed decision threshold is used, then the system complexity is low, but the system cannot adapt to changing environmental conditions
Solution Approach 1:
The system performs self-adjustment by automatically monitoring its own performance and modifying the decision threshold without external intervention. This self-service capability enables the system to adapt to changing environmental conditions while maintaining relatively low complexity, as the adjustment process is automated rather than requiring manual reconfiguration.
Solution Approach 2:
The patent transforms the static decision threshold into a dynamic parameter that automatically adapts to environmental changes. This dynamic approach enables the system to maintain high adaptability while keeping complexity manageable through automated adjustment mechanisms that respond to monitored performance metrics.
Data Source
AI summary
A computer program product and a method perform various operations including: capturing face images of people at a particular location during a current period; performing, for each of the face images captured during the current period, face recognition using a value of a decision threshold to make a determination whether the captured face image and a stored face image in a database of stored face images are of the same person; calculating a current positive determination rate for the face images captured during the current period; and automatically adjusting the value of the decision threshold for performing the face recognition during a subsequent period following the current period. The value of the decision threshold is increased (decreased) for performing the face recognition during the subsequent period in response to the current positive determination rate being greater than (less than) a target positive determination rate during the current period.