Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fingerprint quality evaluation method based on ridge quality expert visual cognitive machine learning

A quality evaluation and machine learning technology, applied in the direction of acquiring/organizing fingerprints/palmprints, instruments, computer parts, etc., to solve the problems of incomplete fingerprint image quality evaluation and difficult quantitative description of fingerprint image quality evaluation.

Active Publication Date: 2018-12-14
张威 +1
View PDF6 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned quality evaluation problems of imprinted fingerprint images, to overcome the incomplete evaluation of fingerprint image quality by existing standardization organizations such as NIST, and to use artificial intelligence related methods to learn the basis and feelings of fingerprint identification experts who are not easy to quantitatively describe the quality evaluation of fingerprint images, Quickly, comprehensively and comprehensively give the comprehensive quality of fingerprint images

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fingerprint quality evaluation method based on ridge quality expert visual cognitive machine learning
  • Fingerprint quality evaluation method based on ridge quality expert visual cognitive machine learning
  • Fingerprint quality evaluation method based on ridge quality expert visual cognitive machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0089] The present invention will be further described in detail below in conjunction with the examples.

[0090] like figure 1 As shown, the method includes the following steps:

[0091] Step S01: Reconstruction of the leftover area of ​​ridges on the extracted data

[0092] This step conducts dynamic big data sampling analysis and information mining on the information stored in the existing Automatic Fingerprint Identification System (AFIS) database of the forensic science department, and obtains data that can dynamically and objectively reflect "comparison of multi-genre algorithms" and "most fingerprint experts". "Visual inspection and identification" of the local ridge image of the printed fingerprint and the "double-cusp quality requirements" of the printed fingerprint specific area ridge image, as the next step, provide the data source for fingerprint experts to carry out visual cognitive marking.

[0093] Step S01.1: The data to be extracted mainly include:

[0094]...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a fingerprint quality evaluation method based on ridge quality expert visual cognitive machine learning. The method includes carrying out expert cognition and quality markingon the image quality grade of the fingerprint ridge line in the fingerprint ridge line remaining position reconstruction area, carrying out expert individual quality evaluation stability analysis andexpert quality evaluation mode cluster analysis on the quality marking data, and obtaining the priority of each expert quality marking data; partitioning the expert quality marker data and applying the same to the training of neural network model for image quality evaluation according to the priority. The neural network model is constructed and trained to evaluate the quality of the local blocks,and the accuracy threshold is set. By using the local block quality evaluation data of the neural network model, the global comprehensive quality evaluation of the fingerprint image is calculated. Theinvention is widely applied to the image quality evaluation of heterogeneous fingerprints of various specifications, taking into account the double-tip requirements of the multi-school fingerprint comparison algorithm and the expert fingerprint identification for fingerprint quality.

Description

technical field [0001] The invention provides a fingerprint quality evaluation method based on machine learning of ridge quality experts' visual cognition, which belongs to the field of biometric identification, can be used for multi-comparison algorithm architecture, data quality control of ultra-large-scale fingerprint systems, and can be optimized and compared in AFIS system The risk analysis of missing checks, the targeting range of missed checks, and the evaluation of the practical application value of various heterogeneous fingerprint image data (such as immigration, ID card, driver, examinee fingerprint registration, etc.) in the field of forensic science are of outstanding significance. Background technique [0002] Compared with other biometric identification technologies, fingerprint automatic identification technology has excellent accuracy and high economic practicability, and the current application prospect is very clear. The purpose of building the fingerprint...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T7/00G06K9/00G06K9/62
CPCG06T7/0002G06T2207/20084G06T2207/20081G06T2207/20021G06T2207/30168G06V40/1347G06V40/13G06V40/1365G06F18/23213
Inventor 张威王威
Owner 张威
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com