Partial Fingerprint Sensor Signal Analysis for Motion Noise Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing partial fingerprint sensors are sensitive to variations in user technique, leading to inaccurate finger motion tracking and noise issues during fingerprint scanning, particularly when users swipe their fingers at non-uniform speeds or pause during the scanning process.
Innovation Solution
Improved signal analysis techniques and algorithms are employed to accurately determine finger location and movement by analyzing sensor signals for stable and noisy regions, allowing for more precise interpolation and correction of partial fingerprint images, even when swiped optimally or not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If partial fingerprint sensors are used to reduce size and cost, then device miniaturization is achieved, but sensitivity to user technique variations increases
Solution Approach 1:
The fingerprint sensor is divided into multiple linear sensor arrays that scan the fingerprint in segments. By combining data from these segmented scans with motion detection, the system achieves full fingerprint capture while maintaining the miniaturized sensor design and improving reliability through multiple measurement passes.
Solution Approach 2:
Motion detection algorithms provide feedback about finger movement speed and position to the fingerprint scanning system. This feedback enables real-time adjustment of scanning parameters and allows the system to compensate for non-uniform finger motion, thereby maintaining tracking accuracy despite the reduced sensor size.
2Loss of information
If finger motion tracking is performed to capture complete fingerprint, then imaging completeness is improved, but noise from non-uniform swiping increases
Solution Approach 1:
The system extracts and separates motion information from fingerprint imaging data by using dedicated motion detection sensor arrays. This extraction allows the fingerprint scanning algorithm to distinguish between useful fingerprint signals and noise caused by non-uniform finger motion, thereby maintaining data completeness while reducing harmful noise effects.
Solution Approach 2:
The system dynamically changes scanning parameters based on detected motion characteristics. When non-uniform motion is detected, the system adjusts scan speed, resolution, and processing algorithms in real-time to optimize the balance between capturing complete fingerprint data and minimizing noise from variable swiping speeds.
3Reliability
If signal analysis is improved to handle non-uniform swiping, then resistance to user technique variations increases, but processing complexity increases
Solution Approach 1:
The signal processing is segmented into distinct modules: motion detection, fingerprint imaging, and data fusion. Each module handles specific aspects of the complex signal analysis independently, making the overall system more manageable and maintainable while improving resistance to user technique variations through specialized processing algorithms.
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
The solution enhances the accuracy of fingerprint scanning by reducing noise and improving resistance to variations in user technique, enabling more reliable and efficient fingerprint capture on portable devices, such as smartphones and laptops.
Implementation Method 1
a first sensor array configured to sense a first set of features of a fingerprint along an axis of finger motion and to generate a first set of image data
Data Source
AI summary
Enhanced accuracy finger position and motion sensors devices, algorithms, and methods are disclosed that can be used in a variety of different applications. The sensors can be used in conjunction with partial fingerprint imagers to produce improved fingerprint scanners. Such improved scanners can use image analysis techniques, such as interpolation between partial fingerprint images to correct for missing data, or discarding redundant partial fingerprint image data, to produce adequate fingerprint images even when the finger has not been applied to the sensor using an optimum technique.