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.

Description

CLAIM OF PRIORITY[0001] This application makes reference to, incorporates the same herein, and claims all benefits accruing under 35 U.S.C. Section 119 from an application for "Method of Recognizing Human Iris Using Daubechies Wavelet Transform," filed earlier in the Korean Industrial Property Office on Mar. 6, 2001, and there duly assigned Serial No. 2001-11440.[0002] 1. Field of Invention[0003] The present invention relates to a method of recognizing the human iris and, more particularly, to a method of recognizing the human iris using the Daubechies wavelet transform to reduce the dimensions of characteristic vectors to improve the processing time.[0004] 2. Description of the Related Art[0005] An iris recognition system is used for performing the identification of an individual based on the information obtained from the analysis of the iris patterns, which are different for each individual. The iris recognition system has superior identification accuracy and thus provides excelle...

AI Technical Summary

Benefits of technology

[0011] The present invention is directed to a method of forming low capacity characteristic vectors, so that the false acceptance rate (FAR) and the false rejection rate (FRR) can be remarkably reduced as compared to the conventional Harr wavelet transform. To this end, the iris features from inputted iris image signals are extracted using the Daubechies wavelet transform.

[0012] One aspect of the present invention provides a method for measuring the similarity between the characteristic vectors, wherein the low capacity characteristic vectors can be properly used for the similarity measurement while the loss of information can be minimized.

[0014] According to another aspect of the present invention, the iris image is acquired through an image acquisition device utilizing a halogen lamp as an illuminator. By repeatedly performing the Daubechies wavelet transform of the inputted iris image, the iris image is multi-divided, and the iris features with optimized sizes are extracted. The characteristic vector, which is effective in displaying and processing the image, is then formed by quantizing the extracted characteristic values. Furthermore, the dimension of the characteristic vector is reduced by quantizing the extracted characteristic values into binary values--that is, when a low capacity characteristic vector is formed, the method of measuring the similarity between the weight registered and the inputted characteristic vectors is used to prevent the reduction of acceptance resulting from the formation of the low capacity characteristic vector. The user authenticity is, therefore, determined by the foregoing method.

Problems solved by technology

However, the Harr wavelet transform has disadvantages in that the characteristic values change irregularly and rapidly.

In addition, a high resolution of the images cannot be obtained if the images are decompressed again after they have been compressed.

However, as the Daubechies wavelet functions are generally more complicated than the Harr wavelet functions, there is a disadvantage in that a larger arithmetic quantity may be needed.

Thus, there is a problem in that practicability and efficiency are undermined when the Gabor transform is used in the field if low capacity information is required.

The Hamming distance (HD) is also excellent for application with the extracted iris features by subdividing the data, but it is not suitable when low capacity data is to be used.

In addition, there are disadvantages in that the formation of the reference characteristic vectors through generalizing the pattern information cannot be easily made, and one can not rely upon the information characteristics of each dimension of the characteristic vectors.

Accordingly, if the low capacity characteristic vectors are used, the accuracy of differentiating characteristic vectors is poor due to an increase in lost information.

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