Biometric Recognition Re-Recognition Mechanism
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Solution Overview
Problem
Biometric recognition systems, such as fingerprint recognition, often fail to accurately authenticate users due to variations in data collected at different moments, leading to incorrect match failures and potential device locking, which affects user experience.
Innovation Solution
A method that performs an initial recognition operation and, upon failure, conducts a re-recognition using data collected at a different moment within the same biological feature recognition process, with the option to continue re-recognition until a successful match or a preset upper limit is reached, thereby reducing false negatives and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recognition operation is performed, then the recognition process is fast, but the accuracy is low due to false negatives
Solution Approach 1:
The system performs a preliminary recognition operation and, upon detecting a match failure, automatically initiates a re-recognition operation. This preliminary action approach ensures that false negatives are caught and corrected through the subsequent re-recognition, thereby improving overall recognition accuracy without requiring multiple simultaneous recognition processes
Solution Approach 2:
The system implements a feedback mechanism where the result of the first recognition operation determines whether a second recognition operation is performed. When the first recognition fails to match, the system feeds back this failure information and triggers another recognition attempt, creating a closed-loop system that improves accuracy by learning from initial recognition failures
2Measurement precision
If multiple recognition operations are performed, then the accuracy is improved, but the resource consumption increases
Solution Approach 1:
The system performs a preliminary recognition operation first, and only initiates a second recognition operation if the first one fails. This approach ensures that most successful recognitions consume minimal energy, while only borderline cases require additional energy expenditure for re-recognition, thereby optimizing the energy-accuracy tradeoff
Solution Approach 2:
The system changes the operational parameters by performing recognition operations conditionally rather than continuously. By adjusting the number of recognition operations based on the outcome of previous operations, the system optimizes energy consumption while maintaining high recognition accuracy through selective re-recognition
3Measurement precision
If re-recognition is performed until success, then the accuracy is maximized, but the processing time increases
Solution Approach 1:
The system performs a preliminary recognition operation and only proceeds to re-recognition if the first attempt fails. This preliminary action strategy ensures that most recognitions are completed quickly in the first attempt, maintaining high productivity, while still providing a safety net through re-recognition to maximize accuracy for difficult cases
Solution Approach 2:
The system performs a partial re-recognition operation only when necessary, rather than continuously performing multiple recognition operations for every case. This partial action approach balances productivity and accuracy by applying additional recognition attempts only to the subset of cases that require them
Data Source
AI summary
A method of recognizing a biological feature is provided. In an example, the method includes: first biological feature data is obtained; a first recognition operation is performed according to the first biological feature data and biological feature template data to obtain a first recognition result; when the first recognition result indicates a match failure, second biological feature data is obtained; and a re-recognition operation is performed according to the second biological feature data and the biological feature template data to obtain a second recognition result. The second biological feature data and the first biological feature data are collected by a same biological feature collector at different moments in a same biological feature recognition process.