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                            Library & Multimedia
 Robust memory-efficient data level information fusion of multi-modal biometric images
NCJ Number
              305879

Author(s)
          Afzel Noore;
                        Richa Singh;
                        Mayank Vatsa
Date Published

2007
Length
              10 pages
Annotation
              This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image.
Abstract
              The proposed approach reduces the memory size, increases the recognition accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, JPEG 2000, and filtering due to tampering. The fusion algorithm is validated using the verification algorithms the authors developed, existing algorithms, and commercial algorithm. In addition to the multi-modal database, experiments are also reformed on other well-known databases such as FERET face database and CASIA iris database. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The results show that the fusion process reduced the memory required for storing the multi-modal images by 75%. The integrity of biometric features and the recognition performance of the resulting composite fused image is not affected significantly. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. The authors also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images. (Published abstract provided)
      
  
          Additional Details
Grant Number(s)
        2003-RC-CX-K001


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Award
        Forensic Science Initiative 2003
    
  
  
Date Published: January 1, 2007
    
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