Iris recognition method based on wavelet packet decomposition

Abstract

The invention discloses an iris recognition method based on wavelet packet decomposition, and the method comprises the steps: collecting a human eye image, carrying out the iris image preprocessing, obtaining a processed iris image, modulating the diagonal high-frequency information of the wavelet packet decomposition of the iris image into an iris feature code through a threshold value, extracting the iris features, and finally, adopting a Hamming distance classifier to perform classification and matching calculation on iris features, and judging whether the two feature codes are from the same iris or not by calculating the similarity between the current iris feature code and iris codes in an iris template library, so that iris images are classified. The iris features extracted through the iris feature recognition method have good classification performance, so that the iris recognition precision is improved.

Description

technical field [0001] The invention belongs to the technical field of biological identification, and in particular relates to an iris identification method based on wavelet packet decomposition. Background technique [0002] Iris recognition is a kind of biometric recognition technology, which has higher recognition rate and security than fingerprint, face and other biometric technologies. Feature extraction is a key link in iris recognition. The characteristics of identity identification are extracted from iris texture, and then identity confirmation or identification is carried out by pattern recognition methods. The early iris recognition algorithms mainly include: Daugman's two-dimensional Gabor method, Wildes' Gauss-Laplace pyramid method, and the Daugman method tends to decrease significantly when recognizing iris images with poor quality, and the algorithm is only from a qualitative point of view The features representing the iris are lacking in representing the det...

AI Technical Summary

Problems solved by technology

Lim uses the Haar wavelet as the mother wavelet to filter and decompose the normalized area of ​​the iris four times, and modulates the high-frequency area of ​​the fourth layer obtained by the decomposition, and forms 87 bits with the mean value of the high-frequency coefficients of the wavelet decomposition of the first to third layers Iris feature coding uses the learning vector quantization (LVQ) network to classify the extracted features, and the recognition rate is 98.4%. The iris feature space extracted by Lim's algorithm is small, the operation speed is fast and the storage space is saved, but Only 87 binary bits are used to represent the iris texture, and the recognition rate under massive data is difficult to guarantee

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