Iris image quality classification method based on BP (back propagation) network and wavelet transformation

Abstract

The invention discloses an iris image quality classification method based on a BP (back propagation) network and wavelet transformation. The method includes building a BP neural network model for iris image quality classification; selecting multiple iris image samples and subjecting the iris image samples to wavelet transformation to extract characteristic values of wavelet transformation coefficients; inputting the extracted characteristic values of the wavelet transformation coefficients into the BP neural network model for training; classifying iris image quality by the trained BP neural network model; inputting the characteristic values of the wavelet transformation coefficients, extracted from the iris image samples, into the trained BP neural network model; if an output result of the BP neural network model is as same as an expectation value of an available iris, judging the currently-input iris quantity to be available, or otherwise, judging the currently-input iris quantity to be non-available.

Description

technical field The invention belongs to the field of image recognition, in particular to a method for classifying iris image quality based on BP network and wavelet transform. Background technique Iris recognition is a new biometric recognition method developed in recent years. Compared with other biometric identification methods such as fingerprint recognition, face recognition and voice recognition, iris recognition has stronger advantages in terms of accuracy, uniqueness, stability, collectability and non-invasiveness. In practical applications, the recognition effect of the iris recognition system is closely related to the quality of the collected images. Rainbow recognition system requires people to cooperate with equipment. However, there are some uncertain and unstable factors between equipment and people. In the process of iris image acquisition, uneven light conditions, changes in the distance between the human eye and the acquisition device, and the physiologi...

AI Technical Summary

Problems solved by technology

However, because the iris image is affected by contrast, illumination, interference, etc., there are some unstable features in the extracted iris feature template, which increases the difficulty of iris recognition.

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