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                            Library & Multimedia
 LQM: Localized Quality Measure for Fingerprint Image Enhancement
NCJ Number
              253983
Journal

*Ieee Access* Volume: 7 Dated: 2019 Pages: 104567-104576
Author(s)
          Karen Panetta;
                        Shreyas Kamath;
                        Srijith Rajeev;
                        Sos S. Agaian
Date Published

2019
Length
              10 pages
Annotation
              This article reports on the development of a novel localized quality measure (LQM) to evaluate the quality of fingerprint images, as well as a genetic localized quality measure enhancement (LQME) algorithm, which is tailored to iteratively enhance poor-quality fingerprint images.
Abstract
              In addition, a method is introduced that automatically chooses the enhancement algorithm's parameters based on the proposed measure, such that it yields the best enhancement result. Fingerprint is one of the most widely used biometric in law enforcement; however, low-quality fingerprint images can drastically degrade the performance of automated fingerprint identification systems (AFIS). AFIS can be substantially advanced by establishing a metric to evaluate the image quality accurately and then using this metric to enable an automated enhancement process. The LQM measure presented in the current project uses fingerprint image characteristics that include sharpness, contrast, orientation certainty level, symmetry features, and imprints of friction ridge structure (minutiae) information. The FVC2004 Set B database containing fingerprint images from four different sensors and a total of 240 images (80 from each sensor) is used to evaluate the performance of the presented algorithms and methods. The computer simulations demonstrate that the LQM measure is useful in predicting the quality of the fingerprint images captured from various devices. Furthermore, the experiments show that LQME can recover retrievable-corrupt fingerprint regions. (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2014-IJ-CX-K003


  
  
        
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        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Evaluation of The Use of A Non-Contact, 3D Scanner for Collecting Postmortem Fingerprints
    
  
  
Date Published: January 1, 2019
    
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