www.pudn.com > iriscode.zip > gethammingdistance.m

% gethammingdistance - returns the Hamming Distance between two iris templates 
% incorporates noise masks, so noise bits are not used for 
% calculating the HD 
% 
% Usage:  
% [template, mask] = createiristemplate(eyeimage_filename) 
% 
% Arguments: 
%	template1       - first template 
%   mask1           - corresponding noise mask 
%   template2       - second template 
%   mask2           - corresponding noise mask 
%   scales          - the number of filters used to encode the templates, 
%                     needed for shifting. 
% 
% Output: 
%   hd              - the Hamming distance as a ratio 
% 
% Author:  
% Libor Masek 
% masekl01@csse.uwa.edu.au 
% School of Computer Science & Software Engineering 
% The University of Western Australia 
% November 2003 
 
 
function hd = gethammingdistance(template1, mask1, template2, mask2, scales) 
 
template1 = logical(template1); 
mask1 = logical(mask1); 
 
template2 = logical(template2); 
mask2 = logical(mask2); 
 
hd = NaN; 
 
% shift template left and right, use the lowest Hamming distance 
for shifts=-8:8 
     
    template1s = shiftbits(template1, shifts,scales); 
    mask1s = shiftbits(mask1, shifts,scales); 
     
     
    mask = mask1s | mask2; 
     
    nummaskbits = sum(sum(mask == 1)); 
     
    totalbits = (size(template1s,1)*size(template1s,2)) - nummaskbits; 
     
    C = xor(template1s,template2); 
     
    C = C & ~mask; 
    bitsdiff = sum(sum(C==1)); 
     
    if totalbits == 0 
         
        hd = NaN; 
         
    else 
         
        hd1 = bitsdiff / totalbits; 
         
         
        if  hd1 < hd || isnan(hd) 
             
            hd = hd1; 
             
        end 
         
         
    end 
     
end