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                            Library & Multimedia
 A selective feature information approach for iris image-quality measure
NCJ Number
              305369
Journal

*IEEE Transactions on Information Forensics and Security* Volume: 3 Issue: 3 Dated: Sept 2008 Pages: 572-577
Author(s)
          Craig Belcher;
                        Yingzi Du
Date Published

September 2008
Length
              6 pages
Annotation
              This research article examines image quality and the impact on accuracy of positive identification in iris-recognition systems.
Abstract
              Poor quality images can significantly affect the accuracy of iris-recognition systems because they do not have enough feature information. However, existing quality measures have focused on parameters or factors other than feature information. The quality of feature available for measure is a combination of the distinctiveness of the iris region and the amount of iris region available. Some irises may only have a small area of changing patterns. Due to this, the proposed approach automatically selects the portions of the iris with the most distinguishable changing patterns to measure the feature information. The combination of occlusion and dilation determines the amount of iris region available and is considered in the proposed quality measure. The quality score is the fused result of the feature information score, the occlusion score, and the dilation score. The relationship between the quality score and recognition accuracy is evaluated using 2-D Gabor and 1-D Log-Gabor wavelet approaches and validated using a diverse data set. In addition, the proposed method is compared with the convolution matrix, spectrum energy, and Mexican hat wavelet methods. These three methods represent a variety of approaches for iris-quality measure. The experimental results show that the proposed quality score is highly correlated with the recognition accuracy and is capable of predicting the recognition results. (Publisher abstract provided)
      
  
          Additional Details
Grant Number(s)
        2007-DE-BX-K182


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Selective Feature Based Quality Measure Plug-in for Iris Recognition System
    
  
  
Date Published: September 1, 2008
    
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