Fingerprint Sensor Signal Tracking for Stable Image Capture
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Solution Overview
Problem
Capacitive fingerprint sensors face challenges in accurately determining the right time for capturing a full fingerprint image, often resulting in images being acquired too early or too late, leading to insufficient data and requiring user reactivation, due to limited timing windows and sensitivity to finger movement and humidity.
Innovation Solution
A method that tracks the spatio-temporal evolution of signal levels from a fingerprint sensor, allowing for accurate estimation of stable conditions for image capture by computing statistical indicators and dynamically adjusting sensor settings to prevent saturation, enabling fast and reliable fingerprint image acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a full fingerprint image is acquired as quickly as possible, then productivity is improved, but measurement precision deteriorates because the image may be captured too early or too late
Solution Approach 1:
The system performs preliminary actions by acquiring multiple sub-images at different time points before capturing the full fingerprint image. These sub-images are used to compute statistical indicators that predict the optimal timing for full image capture, thereby preparing the system in advance to avoid too early or too late capture
Solution Approach 2:
The system implements feedback by continuously monitoring signal levels from sub-images and computing statistical indicators (mean, standard deviation, slope) to determine when stable conditions are achieved. This feedback mechanism adjusts the capture timing dynamically based on real-time signal analysis, ensuring optimal capture moment is identified
2Ease of operation
If the capture time window is extended to accommodate finger movement, then ease of operation is improved, but manufacturing precision deteriorates because sweat spreads and image quality degrades
Solution Approach 1:
The system applies dynamics by making the capture timing adaptive rather than fixed. It dynamically adjusts the optimal capture moment based on real-time analysis of sub-image signal levels and statistical indicators, allowing the system to accommodate natural finger movement while maintaining image quality through intelligent timing selection
3Measurement precision
If sensor settings are fixed to maximize signal, then measurement precision is improved, but object-generated harmful factors worsen due to signal saturation
Solution Approach 1:
The system changes parameters by dynamically adjusting sensor settings based on signal level analysis. It monitors signal intensity from sub-images and modifies sensor parameters accordingly to maintain optimal detection range, preventing both insufficient signal detection and signal saturation through adaptive parameter control
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise timing of fingerprint image capture, improving the quality and reliability of fingerprint verification by adapting to changing conditions such as humidity and finger movement, reducing the need for user reactivation and enhancing the dynamic range of the fingerprint sensor.
Implementation Method 1
a capacitive sensor, that can generate an image of the pattern of ridges and valleys
Data Source
Figure 1
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AI summary
An apparatus and a computer-implemented method of acquiring a fingerprint image from a fingerprint sensor with an array of sensor elements spanning a sensing area, comprising: monitoring groups of sensor elements located at group-wise spaced apart positions in the array of sensor elements, to determine a touch event occurring on the array of sensor elements; from the array of sensor elements, acquiring, at respective points in time, fingerprint sub-images which are confined in size to a subarea of the sensing area;as the fingerprint sub-images are acquired, computing values of a statistical indicator for the fingerprint sub-images; and acquiring a full fingerprint image when a predefined criterion indicates that the values of the statistical indicator has reached or is about to reach a stable state.