Fingerprint Sensor Compensation Using Characteristic Function Coefficients
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Solution Overview
Problem
Fingerprint recognition devices face reduced accuracy due to noise distortion in fingerprint images, leading to increased memory requirements for compensation data as the number of test patterns increases, which elevates manufacturing costs and reduces productivity.
Innovation Solution
A display apparatus with a fingerprint sensor and controller that generates a compensation characteristic function using test patterns, extracting coefficients to compensate fingerprint sensing data without increasing memory capacity, even as the number of test patterns grows, by storing coefficient compensation values independently of the number of test patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of test patterns is increased to enhance the reliability of fingerprint recognition, then the measurement precision is improved, but the quantity of substance (memory capacity) increases
Solution Approach 1:
The patent extracts only the essential compensation characteristics from multiple test patterns by fitting a characteristic function (e.g., y=Ax+B) to the test data. Instead of storing all raw compensation data from multiple test patterns, only the coefficients (A and B) are stored in memory, dramatically reducing memory capacity while maintaining fingerprint recognition accuracy.
Solution Approach 2:
The patent transforms the compensation data from raw pixel-level correction values into parameterized characteristic function coefficients. By changing the representation from detailed compensation maps to simplified mathematical parameters (A and B coefficients), the system achieves the same compensation effect with minimal memory storage.
2Reliability
If the number of test patterns is increased to enhance the reliability of fingerprint recognition, then the reliability is improved, but the quantity of substance (memory capacity) increases
Solution Approach 1:
The patent extracts the essential compensation characteristics from multiple test patterns by fitting a characteristic function (e.g., y=Ax+B) to the test data. Instead of storing all raw compensation data from multiple test patterns, only the coefficients (A and B) are stored in memory, dramatically reducing memory capacity while maintaining fingerprint recognition accuracy.
Solution Approach 2:
The patent creates a simplified mathematical model (characteristic function) that copies the essential compensation behavior of multiple test patterns. This model uses only a few coefficients to represent the compensation characteristics, allowing reliable fingerprint recognition without storing large amounts of original test pattern data.
3Reliability
If the number of test patterns is increased to enhance the reliability of fingerprint recognition, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The patent transforms the compensation data from raw pixel-level correction values into parameterized characteristic function coefficients. By changing the representation from detailed compensation maps to simplified mathematical parameters (A and B coefficients), the system achieves the same compensation effect with minimal memory storage and simpler processing.
Solution Approach 2:
Instead of directly using multiple test patterns to compensate fingerprint images (which would increase complexity), the patent inverts the approach by first fitting a characteristic function to the test patterns and then using this simplified function for compensation. This reversal simplifies the overall system complexity while maintaining reliability.
Data Source
AI summary
A display apparatus includes: a display panel configured to display an image; a fingerprint sensor disposed on the display panel and configured to capture a fingerprint image and to generate fingerprint sensing data; and a fingerprint sensor controller configured to generate a compensation characteristic function of each pixel of the fingerprint sensor using a plurality of test patterns, to extract a coefficient of the compensation characteristic function of each pixel and to compensate the fingerprint sensing data using the coefficient of the compensation characteristic function.