Method of recognizing human iris using daubechies wavelet transform

Abstract

The present invention relates to a method of recognizing the human iris using the Daubechies wavelet transform. The dimensions of characteristic vectors are initially reduced by extracting iris features from the inputted iris image signals through the Daubechies wavelet transform. Then, the binary characteristic vectors are generated by applying quantization functions to the extracted characteristic values so that the utility of human iris recognition can be improved as the storage capacity and processing time thereof can be reduced by generating low capacity characteristic vectors. By measuring the similarity between the generated characteristic vectors and the previously registered characteristic vectors, characteristic vectors indicative of the iris patterns can be realized.

What is claimed is:  
     
         1 . A method of recognizing a human iris using the Daubechies wavelet transform, the method comprising the steps of: 
 (a) obtaining an iris image from a user's eye using an image acquisition device;    (b) repeatedly performing said Daubechies wavelet transform on said iris image so as to multi-divide said iris image for a predetermined number of times;    (c) extracting image with high frequency components from said multi-divided image so as to extract iris features;    (d) extracting characteristic values of a characteristic vector from said extracted image with said high frequency components;    (e) generating a binary characteristic vector by quantizing said extracted characteristic values; and,    (f) determining whether said user as an enrollee by measuring a similarity between said generated characteristic vector and a previously registered characteristic vector.    
     
     
         2 . The method of  claim 1 , further comprising the step of illuminating said user's eye.  
     
     
         3 . The method of  claim 2 , wherein the step of illuminating said user's eye comprises the step of placing a halogen lamp at both ends of said user's eye.  
     
     
         4 . The method of  claim 1 , wherein said step (b) comprises the steps of: extracting a region HH from said multi-divided image having said high frequency components in both x and y directions; storing information of said region HH for use in extracting iris features; performing multi-division of a region LL from said multi-divided image having low frequency components in both x and y directions.  
     
     
         5 . The method of  claim 2 , wherein said predetermined number of times is set at four.  
     
     
         6 . The method of  claim 1 , wherein said step (c) comprises the steps of: receiving multi-divided images of a plurality of high frequency regions HH i  formed by said multi-division in said step (b); calculating the average values of regions HH 1  to HH n−1  excluding the last region HH N ; assigning said calculated average values to the components of said characteristic vector, respectively; assigning said calculated value M of said last region HH N  to the components of said binary characteristic vector; combining said N−1 average values and said M values so as to generate a (M+N−1)-dimensional characteristic vector; and, quantizing all values of said generated characteristic vector into binary values so as to generate a final (M+N−1)-dimensional characteristic vector.  
     
     
         7 . The method of  claim 1 , wherein said step (f) comprises the steps of: applying predetermined weights to the i-th dimensions of said generated characteristic vector generated from said step (c) and said previously registered characteristic vector; calculating the inner product S of said two weighted characteristic vectors; and determining said user as an enrollee if said inner product S is more than a verification reference value C.  
     
     
         8 . The method of  claim 1 , wherein said image acquisition device comprises a halogen lamp.