Terminal Fingerprint Unlocking via Capacitance Auto Control
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Solution Overview
Problem
Current fingerprint recognition technologies face challenges in reducing the unlocking time of mobile terminals, which is a competitive factor for manufacturers, and are also affected by factors like hand instability and wet fingers, leading to increased False Rejection Rates (FRR).
Innovation Solution
The method involves detecting a user's finger touch and determining the stability and wetness of the finger using initial fingerprint images, adjusting Capacitance Auto Control (CAC) parameters to receive clearer second fingerprint images, and performing matching processes to unlock the terminal efficiently, thereby reducing the unlocking time and FRR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional fingerprint recognition is used, then the terminal can be unlocked, but the unlocking time is too long
Solution Approach 1:
The system performs preliminary actions by detecting finger touch and capturing initial fingerprint images before formal recognition. It pre-judges finger stability and wetness conditions, and pre-adjusts CAC parameters to optimal values, so that when actual recognition is needed, the process can proceed faster without compromising accuracy.
Solution Approach 2:
The system dynamically adjusts CAC parameters based on real-time finger conditions (stability and wetness). Instead of using fixed parameters, the system adapts the capacitance auto control settings during the recognition process to optimize image quality under varying conditions, thereby reducing retry rates and overall unlocking time.
2Reliability
If multiple fingerprint images are captured to improve accuracy, then the recognition reliability increases, but the unlocking time increases
Solution Approach 1:
The system performs preliminary quality assessment of fingerprint images before they are used for recognition. By pre-judging whether images meet quality standards based on finger stability and wetness conditions, the system avoids using poor-quality images that would require re-capture, thereby reducing the number of actual recognition attempts needed.
Solution Approach 2:
The system uses feedback from initial image quality assessment to control the recognition process. When finger conditions are judged as unstable or wet, the system adjusts CAC parameters and guides users to retry, preventing wasted recognition attempts on poor-quality images and reducing overall unlocking time.
3Measurement precision
If CAC parameters are adjusted for wet fingers, then the image quality improves, but the system complexity increases
Solution Approach 1:
The system implements self-service by automatically detecting finger wetness conditions and autonomously adjusting CAC parameters without user intervention. The system monitors capacitance changes, judges wetness levels, and modifies parameters adaptively, making the complex adjustment process transparent to users while maintaining simple operation.
Solution Approach 2:
The system changes physical parameters (CAC settings) based on detected conditions. By monitoring capacitance variations that indicate wetness, the system dynamically modifies electrical parameters to optimize fingerprint image quality under different environmental conditions, improving measurement precision through parameter adaptation.
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 enhances the speed of unlocking by stabilizing the fingerprint image acquisition and adapting to wet conditions, thereby improving the accuracy and reducing the power consumption of the terminal.
Implementation Method 1
when a touch operation of a finger of a user on a fingerprint recognition sensor of a terminal is detected, at least one first fingerprint image is received
Data Source
AI summary
A method for controlling unlocking is provided. The method includes the following operations. M first fingerprint images are received when a touch operation of a finger of a user on a fingerprint recognition module of a terminal device is detected. N second fingerprint images are received based on N sets of capacity auto control (CAC) parameters when the finger of the user is in a steady state. A first target fingerprint image is determined and a matching process on the first target fingerprint image is performed. The terminal device is unlocked when the first target fingerprint image is matched.