A fingerprint image acquisition method based on bioelectric signal enhancement

Through multi-source bioelectric signal fusion and dynamic optimization technology, the problem of the acquisition quality decline of traditional fingerprint acquisition technology in complex scenarios is solved, and high-resolution and clear texture fingerprint image generation is achieved, which improves recognition accuracy and robustness.

CN120071407BActive Publication Date: 2025-08-29TAIZHOU DEXING ELECTRONIC TECH CO LTD
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Patent Information

Application Number
CN202510135726.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-02-07
Publication Date
2025-08-29
Estimated Expiration
2045-02-07

AI Technical Summary

Technical Problem

Traditional fingerprint acquisition technology has reduced the acquisition quality in complex scenarios (such as wet hands, dry hands, aging skin, low contact pressure, etc.), making it difficult to adjust adaptively. Signal processing and image generation methods lack dynamic changes, resulting in blurred image, incomplete texture, and insufficient recognition accuracy and robustness.

Method used

Multi-source bioelectric signal fusion and dynamic optimization technology are adopted to jointly model and enhance neural signals, electrostatic signals and capacitive signals, and a contact force sensor and displacement sensing module are combined to monitor finger movement in real time to build a dynamic compensation model, and use the generative adversarial network to optimize texture distribution, perform spatiotemporal feature decoding, and generate high-resolution, clear texture fingerprint images.

Benefits of technology

High-precision fingerprint image acquisition is achieved in complex scenarios, with clear texture details, strong adaptability, good robustness and high stability, which significantly improves recognition accuracy and image quality.

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Abstract

The present invention discloses a fingerprint image acquisition method based on bioelectric signal enhancement, comprising the following steps: S1, constructing an original signal dataset; S2, performing dynamic modeling and generating a spatial distribution model of the signal and adaptive enhancement parameters; S3, monitoring finger sliding, rotation, and pressure changes, and generating spatiotemporal distribution data in combination with the spatial distribution model; S4, compensating for motion-induced texture deviations based on the spatiotemporal distribution data to generate a signal distribution model; S5, enhancing signal contrast through neural-fingerprint feature interaction enhancement and neural response synchronous amplification mechanisms to generate an enhanced signal; S6, constructing a multimodal collaborative network to optimize texture distribution characteristics and generate an optimized feature signal; S7, decoding the optimized feature signal using spatiotemporal feature decoding technology to generate a final fingerprint image. The present invention utilizes multi-source bioelectric signal fusion and dynamic optimization technology to achieve high-precision fingerprint image acquisition in complex scenarios.
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