Method for determining quality of iris image based on machine learning

An iris image and determination method technology, which is applied in the fields of instruments, computer parts, character and pattern recognition, etc., can solve the problems such as the inability to accurately reflect the overall quality of the iris image, the quality score is too subdivided, and the indistinguishability is achieved. Improve stability, improve accuracy, reduce the effect of ratio mutation

Active Publication Date: 2012-07-11
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

However, they only considered the effect of a single factor, and ignored the influence of multi-factor joint action on iris image quality. Relatively speaking, there are not many multi-factor joint quality determination methods, and the premise of multi-factor joint is to correctly evaluate each iris image quality. In addition, the quality score after fusion is too subdivided, resulting in no distinction between quality levels. For details, s

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  • Method for determining quality of iris image based on machine learning
  • Method for determining quality of iris image based on machine learning
  • Method for determining quality of iris image based on machine learning

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[0033] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0034] Today, with the rapid development of iris recognition system, the degradation of iris image quality due to changes in acquisition equipment, external environment and iris itself has become a bottleneck in the development of iris recognition (such as figure 2 The situation after six kinds of iris image quality changes are shown), how to select the image suitable for system processing requirements from a large number of acquired iris images becomes particularly important.

[0035] The present invention proposes that the quality factor, quality score and quality grade involved in the iris image quality determination method based on machine learning are defined as follows:

[0036] Quality factor: refers to a single f...

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Abstract

The invention discloses a method for determining the quality of an iris image based on machine learning. The method comprises the following steps of: pre-processing the iris image; extracting quality factors of the iris image; fitting probability density functions of positive and negative samples of single quality factors by using multiple Gaussian models; performing fusion by using an improved Neyman-Pearson method to acquire the quality score of the iris image; and determining an optimal quality level by a hypothesis testing method. The invention provides a robust detecting method aiming at defocusing, motion blur and heterotropia; and multiple quality factors are fused by introducing the Neyman-Pearson method to form the quality score, and the image quality level with statistical significance is acquired by the hypothesis testing method. The method can be used for quality determination when the iris image is acquired and performance prediction aiming at a recognition algorithm.

Description

technical field [0001] The invention relates to computer vision, digital image processing and pattern recognition, in particular to a method for determining the quality of iris images based on machine learning. Background technique [0002] As a reliable identification technology, iris recognition has been widely used, such as identification in airports, customs, and financial institutions. However, due to the limitation of the imaging range of the iris sensor, it is difficult to obtain iris images that meet the requirements without the cooperation of the user, and the image quality has become a bottleneck in the development of iris recognition. In the process of image acquisition, a large number of iris images of different quality are added to the recognition sequence. It is precisely because of the addition of low-quality iris images that the performance of the iris recognition system is greatly reduced. With the development of large-scale and long-distance iris recogniti...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 谭铁牛孙哲南李星光
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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