Touchless Fingerprint Matching via Localized Normalization
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Solution Overview
Problem
Conventional fingerprint authentication methods for mobile devices face challenges such as inconsistent image capture due to varying lighting and depth of field, finger rotation, and processing intensity, which affect the reliability and quality of fingerprint matching in touchless authentication systems.
Innovation Solution
The implementation of localized normalization techniques to enhance fingerprint image quality, combined with key point comparisons for assessing similarity, addresses inconsistencies in finger rotation, scale, and translation during capture, and optimizes processing efficiency by focusing on specific image regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If touchless fingerprint capture is used, then image quality consistency is improved, but lighting variation and depth of field variance affect matching reliability
Solution Approach 1:
The patent applies parameter changes by normalizing the captured fingerprint image through adjusting brightness and contrast parameters. The system dynamically modifies these parameters to compensate for lighting variations and depth of field differences, transforming the image to match reference conditions and thereby maintaining matching reliability despite touchless capture variations.
Solution Approach 2:
The patent introduces an intermediary processing step between image capture and matching. A normalization algorithm acts as a mediator that transforms the variable-quality touchless fingerprint image into a standardized format, enabling reliable matching by bridging the gap between inconsistent capture conditions and consistent authentication requirements.
2Manufacturing precision
If finger is pressed against surface for capture, then image consistency is improved, but oily residue and surface contamination occur
Solution Approach 1:
The patent extracts the harmful contact element by removing the requirement for finger-to-surface contact. The system captures fingerprint images in mid-air without surface interaction, thereby eliminating the source of oily residue and surface contamination while maintaining image consistency through alternative capture geometry.
Solution Approach 2:
The patent replaces the mechanical contact-based capture system with an optical field-based touchless system. Instead of requiring physical pressure and surface contact, the system uses optical imaging to capture fingerprint patterns from a distance, substituting mechanical interaction with optical detection to avoid contamination.
3Extent of automation
If conventional fingerprint algorithms are used, then authentication functionality is achieved, but processing intensity exceeds mobile device capabilities
Solution Approach 1:
The patent segments the fingerprint image processing into distinct stages: capture, normalization, and matching. By dividing the processing workload and applying specialized algorithms at each stage (particularly lightweight normalization), the system reduces overall processing intensity while maintaining authentication functionality suitable for mobile device constraints.
Solution Approach 2:
The patent applies partial action by implementing selective normalization only on critical regions of the fingerprint image rather than processing the entire image at full resolution. This approach provides sufficient authentication accuracy while significantly reducing computational burden and energy consumption on mobile devices.
Data Source
AI summary
In order to authenticate a user of an electronic device, an image of the user's fingerprint is captured. Before feature information is extracted, the fingerprint image is enhanced via localized normalization thereby increasing contrast within the fingerprint image. Thereafter, feature information, such as key point data, is extracted from the image and compared to a predefined template to determine whether the feature information matches the template. If so, the user is authenticated. By enhancing the quality of the fingerprint image through localized normalization and other techniques, the reliability of the matching operation is significantly enhanced. In addition, using key point comparisons for assessing similarity between the feature information and the template helps to address inconsistencies relating to finger rotation, scale, and translation during capture.