Adaptive Fingerprint Calibration for Environmental Changes
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Solution Overview
Problem
Fingerprint recognition systems face challenges in maintaining calibration effectiveness due to environmental changes, leading to degraded unlocking experiences when users wear masks, as the fingerprint calibration image fails to adapt to varying conditions.
Innovation Solution
An adaptive fingerprint recognition method that updates calibration images based on collected fingerprint images, using algorithms to determine and update calibration images dynamically, ensuring they align with the current environment and user's fingerprint characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a preset fingerprint calibration image is used during production, then the fingerprint image can be calibrated to eliminate interference noise, but the calibration effect deteriorates as the external environment changes
Solution Approach 1:
The patent implements dynamic calibration by allowing the calibration image to be automatically updated based on newly collected fingerprint images. The system transitions from a static preset calibration image to a dynamic one that adapts to environmental changes through continuous learning and adjustment, resolving the contradiction between initial calibration accuracy and long-term environmental adaptability.
Solution Approach 2:
The system performs self-calibration by automatically updating its own calibration image using fingerprint images collected from actual usage. This self-service mechanism enables the calibration image to adapt to environmental changes without requiring manual intervention or reconfiguration, maintaining both accuracy and adaptability.
2Reliability
If the fingerprint calibration image is not updated after delivery, then the device structure remains simple, but the fingerprint recognition accuracy deteriorates due to environmental changes
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously collects fingerprint images during normal operation and uses them to update the calibration image. This feedback loop ensures that the calibration image remains accurate despite environmental changes, improving reliability while adding only minimal computational complexity.
Solution Approach 2:
The system changes the parameter of the calibration image over time by incorporating new fingerprint image data. This parameter evolution allows the calibration image to adapt to environmental variations, maintaining high fingerprint recognition accuracy without requiring complex hardware modifications.
3Measurement precision
If calibration processing is performed using an outdated calibration image, then the processing speed remains fast, but the unlocking accuracy deteriorates
Solution Approach 1:
The patent applies partial updates to the calibration image by incorporating only necessary adjustments from new fingerprint images rather than complete recalibration. This approach maintains fast processing speeds while gradually improving accuracy, avoiding the time cost of full recalibration while still adapting to environmental changes.
Data Source
AI summary
A fingerprint recognition method and apparatus are applied to the field of fingerprint recognition technologies. The method includes: collecting a to-be-enrolled fingerprint image; performing, based on a first calibration image, calibration processing on the to-be-enrolled fingerprint image to obtain a first fingerprint image; and determining whether an adaptive calibration image exists; and updating the first calibration image based on the first fingerprint image and a first update coefficient when the adaptive calibration image exists, to obtain a second calibration image; or generating a third calibration image based on the first fingerprint image when no adaptive calibration image exists. The third calibration image is an image obtained by performing summation and averaging on a plurality of frames of fingerprint images, or the third calibration image is an image obtained by updating the first calibration image based on the first fingerprint image.