Fingerprint Image Aggregation for Resolution and Noise Reduction
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Solution Overview
Problem
Fingerprint recognition devices face issues with inadequate resolution, partial images, improper orientation, and noise interference, leading to difficulties in collecting and recognizing fingerprint data, which can impair enrollment and authentication processes.
Innovation Solution
The implementation of techniques that correlate and aggregate fingerprint images to construct a unified, high-quality image by selecting superior frames, removing noise through filtering, and providing user feedback on positioning and orientation to improve image collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the user's finger is positioned too far from the fingerprint recognition device or exerts too much pressure, then the fingerprint image may have inadequate resolution or be too blurry, but collecting adequate fingerprint information becomes difficult
Solution Approach 1:
The system dynamically adjusts the sensing parameters and processing algorithms based on the quality of each captured fingerprint image. It evaluates image quality metrics and adapts the collection process in real-time to compensate for suboptimal positioning or pressure conditions, ensuring adequate fingerprint information is collected despite varying user actions.
Solution Approach 2:
The system changes processing parameters such as image enhancement techniques, filtering algorithms, and quality thresholds based on the detected image quality. When resolution is inadequate or images are blurry, the system applies parameter adjustments to improve the reliability of fingerprint information extraction.
2Measurement precision
If the user's finger is positioned with only part of the finger in proper position, then a partial fingerprint image is collected, but complete fingerprint information cannot be obtained
Solution Approach 1:
The system segments the fingerprint collection process into multiple captures, identifying which portions of the fingerprint are successfully captured in each image. It then correlates and aggregates these partial images to reconstruct the complete fingerprint pattern, ensuring reliable enrollment and recognition despite partial positioning.
Solution Approach 2:
The system merges multiple partial fingerprint images by correlating identifiable features across images and aggregating the data. This combining process reconstructs the complete fingerprint information from fragmented captures, maintaining reliability even when the user's finger is not fully positioned correctly in a single capture.
3Measurement precision
If the fingerprint image is subject to noise from electromagnetic sources or particulate matter, then the quality of the fingerprint image deteriorates, but accurate recognition becomes difficult
Solution Approach 1:
The system extracts and removes noise components from the fingerprint images through filtering algorithms. It identifies and separates noise from actual fingerprint features caused by electromagnetic interference or particulate matter, retaining only the genuine ridge and valley patterns for accurate recognition.
Solution Approach 2:
The system uses the presence of noise as a diagnostic indicator to apply targeted denoising algorithms. By detecting noise characteristics, the system applies appropriate filtering techniques that convert the harmful noise interference into an opportunity to enhance image quality through adaptive noise reduction.
4Measurement precision
If the user's finger is oriented improperly or at an unexpected direction, then the fingerprint image is not easily identified, but proper orientation guidance increases interaction complexity
Solution Approach 1:
The system provides feedback to the user regarding the quality and orientation of the captured fingerprint image. Based on image analysis, it guides the user to adjust finger positioning and orientation to improve identifiability, balancing measurement precision with ease of operation through informative feedback loops.
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 reliability and quality of fingerprint images, ensuring accurate enrollment and recognition by addressing issues of resolution, completeness, and noise, thereby improving the overall fingerprint recognition process.
Implementation Method 1
capacitive sensing allows a fingerprint recognition device to determine the ridges and valleys of the user's finger, in response to relative capacitances measured between the user's finger (such as on the epidermis of the user's finger) and a capacitive plate in the fingerprint recognition device
Data Source
AI summary
A sequence of biometric data images is received, such as, for example, a sequence of fingerprint images, and a set of biometric data images is selected from the sequence of images. The set of images can include one or more segments of at least one image in the sequence of images. One or more portions of at least one image of biometric data in the set of images can be selected to be included in the unified image of biometric data. The unified image of biometric data can be constructed using the one or more portions of the at least one image of biometric data. If the unified image of biometric data is not complete, a user can be prompted for one or more additional images of biometric data.