Method of recognizing human iris using daubechies wavelet transform
First Claim
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.
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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.
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8 Claims
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1. A method of recognizing a human iris using the Daubechies wavelet transform, the method comprising the steps of:
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(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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification