Daubechies wavelet transform of iris image data for use with iris recognition system

Abstract

Disclosed is a method of recognizing human iris using Daubechies wavelet transform, wherein the dimensions of characteristic vectors are reduced by extracting iris features from inputted iris image signals through the Daubechies wavelet transform, binary characteristic vectors are generated by applying quantization functions to the extracted characteristic values so that utility of human iris recognition can be improved since storage capacity arid processing time thereof can be improved since storage capacity characteristic vectors, and a measurement process suitable for the low capacity characteristic vectors is employed when measuring vectors and previously registered characteristic vectors.

1 . A method of processing iris image data, comprising:
 providing data of an iris image for processing;   processing the iris image data so as to provide a reduced iris image data, wherein processing includes conducting a Daubechies wavelet transform multiple times, wherein the reduced iris image data has a smaller size than the iris image data and has a smaller amount of high frequency components than the iris image data;   creating a characteristic vector of the iris image using the reduced image data;   providing a reference characteristic vector of iris image of a preregistered person; and   determining whether the iris image is associated with the preregistered person, using the characteristic vector and the reference characteristic vector.   
     
     
         2 . The method of  claim 1 , wherein processing comprises:
 computing an inner product of the reference characteristic vector and the characteristic vector of the iris image;   comparing the inner product against a predetermined threshold value; and   determining that the iris image is associated with the predetermined person when the inner product is greater than the predetermined threshold value.   
     
     
         3 . The method of  claim 1 , wherein creating the characteristic vector uses quantized pixel values of the reduced iris image data. 
     
     
         4 . The method of  claim 3 , wherein the quantized pixel values comprise at least two positive values and at least two negative values. 
     
     
         5 . The method of  claim 4 , wherein the quantized pixel values comprise one of the at least two positive values has the same absolute value as one of the at least two negative values. 
     
     
         6 . The method of  claim 4 , wherein the quantized pixel values comprise a first positive value and a second positive value, wherein the second positive value is greater than two times of the first positive value. 
     
     
         7 . The method of  claim 1 , wherein conducting each Daubechies wavelet transform produces a plurality data representing transformed images, wherein processing further comprises selecting one from the plurality of transformed image data. 
     
     
         8 . The method of  claim 7 , wherein the reduced iris image data is one of the plurality of transformed image data created in the last Daubechies wavelet transform. 
     
     
         9 . The method of  claim 7 , wherein creating the characteristic vector uses results of the Daubechies wavelet transforms in addition to the reduced iris image data. 
     
     
         10 . The method of  claim 9 , wherein creating the characteristic vector uses at least one non-selected transformed image data in addition to the reduced iris image data. 
     
     
         11 . The method of  claim 10 , wherein the characteristic vector comprises substantially more information representing the reduced iris image data than information representing the at least one non-selected transformed image data. 
     
     
         12 . The method of  claim 7 , wherein each of the plurality of transformed image data is classified one of HH, HL, LH and LL, wherein HH represents high frequency components in a first direction and a second direction in the transformed image, the first and second directions being perpendicular to each other, wherein HL represents a high frequency component in the first direction and a low frequency component in the second direction, wherein LH represents a low frequency component in the first direction and a high frequency component in the second direction, and wherein LL represents low frequency components in the first and second directions, wherein LL is selected among HH, HL, LH and LL for following Daubechies wavelet transform. 
     
     
         13 . The method of  claim 12 , wherein an average value of the piece of transformed image data classified as HH is included in the characteristic vector. 
     
     
         14 . The method of  claim 13 , wherein a total number of the Daubechies wavelet transform is N, wherein the characteristic vector comprises an N−1 number of values representing the HH data pieces. 
     
     
         15 . The method of  claim 1 , wherein the number of the multiple times is from 2 to 7. 
     
     
         16 . The method of  claim 1 , wherein the number of the plurality of times is from 4. 
     
     
         17 . An iris image data processing apparatus, comprising at least one integrated circuit programmed to perform the method of  claim 1 . 
     
     
         18 . The apparatus of  claim 17 , wherein processing comprises:
 computing an inner product of the reference characteristic vector and the characteristic vector of the iris image;   comparing the inner product against a predetermined threshold value; and   determining that the iris image is associated with the predetermined person when the inner product is greater than the predetermined threshold value.   
     
     
         19 . The apparatus of  claim 17 , wherein creating the characteristic vector uses quantized pixel values of the reduced iris image data, and wherein the quantized pixel values comprise at least two positive values and at least two negative values. 
     
     
         20 . The apparatus of  claim 17 , wherein conducting each Daubechies wavelet transform produces a plurality data representing transformed images, wherein processing further comprises selecting one from the plurality of transformed image data, and wherein the reduced iris image data is one of the plurality of transformed image data created in the last Daubechies wavelet transform.