Iris identification method based on image segmentation and two-dimensional wavelet transformation

Abstract

The iris recognition method based on image segmentation and 2D wavelet transformation comprises: dividing the iris positioning into the key-point inner edge and an outer edge positioning; by mapping technology from Cartesian coordinate to polar coordinate, normalizing positioned image into a fixed gray matrix; then taking image segmentation for two times into 18 sub-areas finally; taking 2D wavelet transformation to extract the wavelet coefficient and variance as feature values from main wavelet channel; in matching and recognition algorithm, using reciprocal variance sum and different weighing values to obtain final result. Compared with prior art, this invention has well noise-proof feature and high recognition ratio without losing real-time.

Description

technical field [0001] The invention relates to an iris recognition method based on image segmentation and two-dimensional wavelet transform, which belongs to the technical field of biometric pattern recognition, and in particular relates to an iris feature recognition method. Background technique [0002] With the development of information technology and the wide application of e-commerce, information security has gradually become an important and urgent problem that people are facing. Biometric identification technology, which can be used for identity authentication and information security protection, has been paid more and more attention by people. The so-called biometric identification technology refers to the close combination of computer and high-tech means such as optics, acoustics and biostatistics, and the identification of personal identity by using the inherent physiological and behavioral characteristics of the human body. The iris feature recognition technolo...

AI Technical Summary

Problems solved by technology

Although method 2 overcomes the limitations of previous systems caused by drift, rotation, and scaling, it is also insensitive to brightness changes and noise, and does not require very high quality of captured images. However, since this algorithm only uses a part of the iris texture Feature information, so did not get a high correct recognition rate

Method 3 uses different texture analysis strategies to extract texture features, and obtains a faster running speed, but it describes the iris texture relatively rough, and its correct recognition rate is still not very high

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